Recorded policy result
Policy satisfied
The interval for the score change stays within the allowed loss of 0.1 score points. The subject's score interval also stays at or above the required minimum of 0.6. Across 1,288 complete independent units, the subject's mean rubric score was 0.9039, compared with 0.8945 for the baseline, a change of +0.009317 score points.
What was compared
| Recorded field | Baseline | Subject |
|---|---|---|
| Recorded runDiffers | k2-32b-extraction-final-baseline | k2-32b-extraction-final-subject |
Results and requirements
Extraction judge quality
Scope: Fixed benchmark; equal independent-unit weights
The interval for the score change stays within the allowed loss of 0.1 score points. The subject's score interval also stays at or above the required minimum of 0.6.
- Baseline
- 0.8945 scoreMean normalized rubric score
- Subject
- 0.9039 scoreMean normalized rubric score
- Change
- +0.009317 scorePaired independent-unit effect interval: -0.07946 to +0.09809 score
- Complete independent units
- 1,2881,288 scheduled units; 1,288 cases; 7,728/7,728 completed trials
How to read this comparison
- Change
- The marker shows the observed change between subject and baseline.
- Interval
- The bar shows uncertainty around that change. Its left endpoint is the lower bound; its right endpoint is the upper bound. The interval level does not tell you how often model runs would pass or fail the policy.
- Policy rule
- The paired lower bound (left endpoint) must be at least the policy minimum. The whole interval must lie on the allowed side of the threshold for this bound check to pass.
- No change
- The dashed line marks no change. An interval crossing it includes changes in either direction. A worse observed score describes this run; policy rejection alone does not prove worse performance beyond these cases.
How this interval was calculated
The complete schedule contains 1,288 cases grouped into 1,288 independent units, with 3 ratings per side for each case. Repeated ratings are averaged within each case, then cases within each unit; units receive equal weight.
Two-sided Hoeffding bounds use the declared score range and the number of independent units. Repetitions and cases within a unit do not add independent units. The paired effect is subject minus baseline in normalized rubric score points, not accuracy percentage points.
The declared family error budget is alpha 0.05, shared across 4 interval claims. Each interval receives alpha / family size (0.05 / 4); family confidence is at least 95% under the declared independence and bounded-score assumptions.
This concerns expected judge scores on the fixed benchmark. It does not establish representative production performance or the correctness of the judge's ratings.
| Check | Observed | Required | Result |
|---|---|---|---|
| Schedule completeness | 7728/7728 completed trials | 7728/7728 completed trials (required) | Passed |
| Independent units | 1288 complete / 1288 scheduled | at least 1288 complete independent units (required) | Passed |
| Paired score change interval width | 0.177546666339193 | interval width <= 0.18 (required) | Passed |
| Paired score change | mean 0.009316770186335; lower -0.079456562983261; upper 0.098090103355932; satisfied | lower >= -0.1; adverse if upper < -0.1 (required) | Passed |
| Subject score bound interval width | 0.088773333169597 | interval width <= 0.18 (required) | Passed |
| Subject score bound | mean 0.903856107660455; lower 0.859469441075657; upper 0.948242774245254; satisfied | lower >= 0.6; adverse if upper < 0.6 (required) | Passed |
- Scores use the rubric mapped to 0 through 1; they are not percentages of correct answers. Changes and policy limits use the same score units.
- 1288 independent units; 7728/7728 completed trials.
- Decision role: required.
- Allowed degradation: 0.1 (higher is better).
- Minimum units: 1288; maximum interval width: 0.18.
- Two-sided Hoeffding intervals; family confidence at least 95% (alpha 0.05; comparison family size 4); Bonferroni error allocation alpha / comparison family size per interval.
- Effect is subject minus baseline; tabulated interval endpoints and widths retain analysis precision.
- Inconclusive checks do not establish a bound: completeness, minimum units and precision must be met before an interval can establish satisfaction or an adverse result.
- Baseline and subject means describe the same complete schedule with equal independent-unit weights.
- Repetitions do not increase the number of independent units.
Scope and limitations
- Equal-unit average expected judge score on the fixed scheduled benchmark; the paired effect is subject minus baseline. No population generalization is asserted.
- Details show the first 50 cases by case ID and at most 2000 characters per text excerpt; use report --case-id to select any retained case.
- A retained judgment does not establish model execution or immunity to prompt injection.
- Verification replays retained measurements offline; it does not remeasure the evaluated service or independently establish that every judge rating is correct.
Evidence details
Identities, methods and exact recorded values
Comparison and policy identities
- Baseline run
k2-32b-extraction-final-baseline- Baseline run digest
sha256:3bd6964aab104d6acbccab3608310bf3dcc7f49dbeebeb93a85f0e418cea311f- Baseline attributed artifact
sha256:8bd11ef22999a01345b73399f75df0189ae4b8cf5c19ae69c3a363039c2bd4bf- Baseline evaluator
K2 frozen answer reference 1- Subject run
k2-32b-extraction-final-subject- Subject run digest
sha256:7ba8068c9334ae132d6f373a229fea1b4600e4bce59e380ef17caea4e7b67196- Subject attributed artifact
sha256:a7abb81bd894dec1d114f484d140a05d38a0e539df1b87700e96d447c1055a44- Subject evaluator
K2 frozen answer reference 1- Baseline source digest
sha256:a5e7747b746671a773f155f07cf9e84994090c97fe3735821549639a7edcdff4- Subject source digest
sha256:fffe741cd630f2acec1b3603f6bd8ca049f31874711679fae8aebbf52cedc33d- Baseline artifact
sha256:8bd11ef22999a01345b73399f75df0189ae4b8cf5c19ae69c3a363039c2bd4bf- Subject artifact
sha256:a7abb81bd894dec1d114f484d140a05d38a0e539df1b87700e96d447c1055a44- Plan
9375d6a3bfba9089713a49831d7be52445f2efd4c62cfeec0009242df500b8a8- Intended subject
sha256:a7abb81bd894dec1d114f484d140a05d38a0e539df1b87700e96d447c1055a44- Signer
gpt-5.6-luna-xhigh-heldout-extraction-signer- Signer key
sha256:594439d462e72e3eecf733bbe8e99c50943274b0663fdfbc98129ff343e2468f
Recorded metric and scope identifiers
[
{
"display_metric": "Extraction judge quality",
"display_scope": "Fixed benchmark; equal independent-unit weights",
"metric": "extraction-judge-quality",
"result": 1,
"scope": "Fixed benchmark; equal independent-unit weights"
}
]sgd-dev-13_00000-6-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Hotels_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Location of the hotel\",\"is_categorical\":false,\"name\":\"destination\",\"possible_values\":[]},{\"description\":\"Start date for the reservation\",\"is_categorical\":false,\"name\":\"check_in_date\",\"possible_values\":[]},{\"description\":\"Number of days in the reservation\",\"is_categorical\":false,\"name\":\"number_of_days\",\"possible_values\":[]},{\"description\":\"Name of the hotel\",\"is_categorical\":false,\"name\":\"hotel_name\",\"possible_values\":[]},{\"description\":\"Address of the hotel\",\"is_categorical\":false,\"name\":\"street_address\",\"possible_values\":[]},{\"description\":\"Phone number of the hotel\",\"is_categorical\":false,\"name\":\"phone_number\",\"possible_values\":[]},{\"description\":\"Price per night for the reservation\",\"is_categorical\":false,\"name\":\"price_per_night\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nAlright. That sounds great. That's all I need for now.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00001-10-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Hotels_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Location of the hotel\",\"is_categorical\":false,\"name\":\"destination\",\"possible_values\":[]},{\"description\":\"Start date for the reservation\",\"is_categorical\":false,\"name\":\"check_in_date\",\"possible_values\":[]},{\"description\":\"Number of days in the reservation\",\"is_categorical\":false,\"name\":\"number_of_days\",\"possible_values\":[]},{\"description\":\"Name of the hotel\",\"is_categorical\":false,\"name\":\"hotel_name\",\"possible_values\":[]},{\"description\":\"Address of the hotel\",\"is_categorical\":false,\"name\":\"street_address\",\"possible_values\":[]},{\"description\":\"Phone number of the hotel\",\"is_categorical\":false,\"name\":\"phone_number\",\"possible_values\":[]},{\"description\":\"Price per night for the reservation\",\"is_categorical\":false,\"name\":\"price_per_night\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nThat's good then. That's all I need.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00002-12-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Hotels_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Location of the hotel\",\"is_categorical\":false,\"name\":\"destination\",\"possible_values\":[]},{\"description\":\"Start date for the reservation\",\"is_categorical\":false,\"name\":\"check_in_date\",\"possible_values\":[]},{\"description\":\"Number of days in the reservation\",\"is_categorical\":false,\"name\":\"number_of_days\",\"possible_values\":[]},{\"description\":\"Name of the hotel\",\"is_categorical\":false,\"name\":\"hotel_name\",\"possible_values\":[]},{\"description\":\"Address of the hotel\",\"is_categorical\":false,\"name\":\"street_address\",\"possible_values\":[]},{\"description\":\"Phone number of the hotel\",\"is_categorical\":false,\"name\":\"phone_number\",\"possible_values\":[]},{\"description\":\"Price per night for the reservation\",\"is_categorical\":false,\"name\":\"price_per_night\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nNo I don't.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00004-4-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"destination_city\",\"value\":\"Chicago\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Flights_3\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"City in which the journey originates\",\"is_categorical\":false,\"name\":\"origin_city\",\"possible_values\":[]},{\"description\":\"City in which the journey ends\",\"is_categorical\":false,\"name\":\"destination_city\",\"possible_values\":[]},{\"description\":\"Number of the airport flying out from\",\"is_categorical\":false,\"name\":\"origin_airport_name\",\"possible_values\":[]},{\"description\":\"Number of the airport flying to\",\"is_categorical\":false,\"name\":\"destination_airport_name\",\"possible_values\":[]},{\"description\":\"Date of departure flight\",\"is_categorical\":false,\"name\":\"departure_date\",\"possible_values\":[]},{\"description\":\"Date of return flight\",\"is_categorical\":false,\"name\":\"return_date\",\"possible_values\":[]},{\"description\":\"Local time of departure of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_departure_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_arrival_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from destination to origin\",\"is_categorical\":false,\"name\":\"inbound_arrival_time\",\"possible_values\":[]},{\"desc",
"subject_answer_excerpt": "\n[{\"slot\":\"destination_city\",\"value\":\"Chicago\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00005-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Flights_3\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"City in which the journey originates\",\"is_categorical\":false,\"name\":\"origin_city\",\"possible_values\":[]},{\"description\":\"City in which the journey ends\",\"is_categorical\":false,\"name\":\"destination_city\",\"possible_values\":[]},{\"description\":\"Number of the airport flying out from\",\"is_categorical\":false,\"name\":\"origin_airport_name\",\"possible_values\":[]},{\"description\":\"Number of the airport flying to\",\"is_categorical\":false,\"name\":\"destination_airport_name\",\"possible_values\":[]},{\"description\":\"Date of departure flight\",\"is_categorical\":false,\"name\":\"departure_date\",\"possible_values\":[]},{\"description\":\"Date of return flight\",\"is_categorical\":false,\"name\":\"return_date\",\"possible_values\":[]},{\"description\":\"Local time of departure of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_departure_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_arrival_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from destination to origin\",\"is_categorical\":false,\"name\":\"inbound_arrival_time\",\"possible_values\":[]},{\"desc",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00006-12-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Hotels_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Location of the hotel\",\"is_categorical\":false,\"name\":\"destination\",\"possible_values\":[]},{\"description\":\"Start date for the reservation\",\"is_categorical\":false,\"name\":\"check_in_date\",\"possible_values\":[]},{\"description\":\"Number of days in the reservation\",\"is_categorical\":false,\"name\":\"number_of_days\",\"possible_values\":[]},{\"description\":\"Name of the hotel\",\"is_categorical\":false,\"name\":\"hotel_name\",\"possible_values\":[]},{\"description\":\"Address of the hotel\",\"is_categorical\":false,\"name\":\"street_address\",\"possible_values\":[]},{\"description\":\"Phone number of the hotel\",\"is_categorical\":false,\"name\":\"phone_number\",\"possible_values\":[]},{\"description\":\"Price per night for the reservation\",\"is_categorical\":false,\"name\":\"price_per_night\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nThat sounds great! That would be all for now.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00008-6-1-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Hotels_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Location of the hotel\",\"is_categorical\":false,\"name\":\"destination\",\"possible_values\":[]},{\"description\":\"Start date for the reservation\",\"is_categorical\":false,\"name\":\"check_in_date\",\"possible_values\":[]},{\"description\":\"Number of days in the reservation\",\"is_categorical\":false,\"name\":\"number_of_days\",\"possible_values\":[]},{\"description\":\"Name of the hotel\",\"is_categorical\":false,\"name\":\"hotel_name\",\"possible_values\":[]},{\"description\":\"Address of the hotel\",\"is_categorical\":false,\"name\":\"street_address\",\"possible_values\":[]},{\"description\":\"Phone number of the hotel\",\"is_categorical\":false,\"name\":\"phone_number\",\"possible_values\":[]},{\"description\":\"Price per night for the reservation\",\"is_categorical\":false,\"name\":\"price_per_night\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\ntwo rooms then",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00012-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"origin_city\",\"value\":\"Vancouver\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Flights_3\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"City in which the journey originates\",\"is_categorical\":false,\"name\":\"origin_city\",\"possible_values\":[]},{\"description\":\"City in which the journey ends\",\"is_categorical\":false,\"name\":\"destination_city\",\"possible_values\":[]},{\"description\":\"Number of the airport flying out from\",\"is_categorical\":false,\"name\":\"origin_airport_name\",\"possible_values\":[]},{\"description\":\"Number of the airport flying to\",\"is_categorical\":false,\"name\":\"destination_airport_name\",\"possible_values\":[]},{\"description\":\"Date of departure flight\",\"is_categorical\":false,\"name\":\"departure_date\",\"possible_values\":[]},{\"description\":\"Date of return flight\",\"is_categorical\":false,\"name\":\"return_date\",\"possible_values\":[]},{\"description\":\"Local time of departure of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_departure_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_arrival_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from destination to origin\",\"is_categorical\":false,\"name\":\"inbound_arrival_time\",\"possible_values\":[]},{\"desc",
"subject_answer_excerpt": "\n[{\"slot\":\"origin_city\",\"value\":\"Vancouver\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00015-4-1-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Hotels_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Location of the hotel\",\"is_categorical\":false,\"name\":\"destination\",\"possible_values\":[]},{\"description\":\"Start date for the reservation\",\"is_categorical\":false,\"name\":\"check_in_date\",\"possible_values\":[]},{\"description\":\"Number of days in the reservation\",\"is_categorical\":false,\"name\":\"number_of_days\",\"possible_values\":[]},{\"description\":\"Name of the hotel\",\"is_categorical\":false,\"name\":\"hotel_name\",\"possible_values\":[]},{\"description\":\"Address of the hotel\",\"is_categorical\":false,\"name\":\"street_address\",\"possible_values\":[]},{\"description\":\"Phone number of the hotel\",\"is_categorical\":false,\"name\":\"phone_number\",\"possible_values\":[]},{\"description\":\"Price per night for the reservation\",\"is_categorical\":false,\"name\":\"price_per_night\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nSounds good. I want to find a hotel room.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00016-4-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"destination_city\",\"value\":\"Sydney\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Flights_3\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"City in which the journey originates\",\"is_categorical\":false,\"name\":\"origin_city\",\"possible_values\":[]},{\"description\":\"City in which the journey ends\",\"is_categorical\":false,\"name\":\"destination_city\",\"possible_values\":[]},{\"description\":\"Number of the airport flying out from\",\"is_categorical\":false,\"name\":\"origin_airport_name\",\"possible_values\":[]},{\"description\":\"Number of the airport flying to\",\"is_categorical\":false,\"name\":\"destination_airport_name\",\"possible_values\":[]},{\"description\":\"Date of departure flight\",\"is_categorical\":false,\"name\":\"departure_date\",\"possible_values\":[]},{\"description\":\"Date of return flight\",\"is_categorical\":false,\"name\":\"return_date\",\"possible_values\":[]},{\"description\":\"Local time of departure of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_departure_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_arrival_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from destination to origin\",\"is_categorical\":false,\"name\":\"inbound_arrival_time\",\"possible_values\":[]},{\"desc",
"subject_answer_excerpt": "\n[{\"slot\":\"destination_city\",\"value\":\"Sydney\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00017-4-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"departure_date\",\"value\":\"7th of March\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Flights_3\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"City in which the journey originates\",\"is_categorical\":false,\"name\":\"origin_city\",\"possible_values\":[]},{\"description\":\"City in which the journey ends\",\"is_categorical\":false,\"name\":\"destination_city\",\"possible_values\":[]},{\"description\":\"Number of the airport flying out from\",\"is_categorical\":false,\"name\":\"origin_airport_name\",\"possible_values\":[]},{\"description\":\"Number of the airport flying to\",\"is_categorical\":false,\"name\":\"destination_airport_name\",\"possible_values\":[]},{\"description\":\"Date of departure flight\",\"is_categorical\":false,\"name\":\"departure_date\",\"possible_values\":[]},{\"description\":\"Date of return flight\",\"is_categorical\":false,\"name\":\"return_date\",\"possible_values\":[]},{\"description\":\"Local time of departure of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_departure_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_arrival_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from destination to origin\",\"is_categorical\":false,\"name\":\"inbound_arrival_time\",\"possible_values\":[]},{\"desc",
"subject_answer_excerpt": "\n[{\"slot\":\"departure_date\",\"value\":\"7th of March\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00018-4-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"origin_city\",\"value\":\"Vancouver\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Flights_3\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"City in which the journey originates\",\"is_categorical\":false,\"name\":\"origin_city\",\"possible_values\":[]},{\"description\":\"City in which the journey ends\",\"is_categorical\":false,\"name\":\"destination_city\",\"possible_values\":[]},{\"description\":\"Number of the airport flying out from\",\"is_categorical\":false,\"name\":\"origin_airport_name\",\"possible_values\":[]},{\"description\":\"Number of the airport flying to\",\"is_categorical\":false,\"name\":\"destination_airport_name\",\"possible_values\":[]},{\"description\":\"Date of departure flight\",\"is_categorical\":false,\"name\":\"departure_date\",\"possible_values\":[]},{\"description\":\"Date of return flight\",\"is_categorical\":false,\"name\":\"return_date\",\"possible_values\":[]},{\"description\":\"Local time of departure of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_departure_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_arrival_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from destination to origin\",\"is_categorical\":false,\"name\":\"inbound_arrival_time\",\"possible_values\":[]},{\"desc",
"subject_answer_excerpt": "\n[{\"slot\":\"origin_city\",\"value\":\"Vancouver\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00027-4-1-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Hotels_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Location of the hotel\",\"is_categorical\":false,\"name\":\"destination\",\"possible_values\":[]},{\"description\":\"Start date for the reservation\",\"is_categorical\":false,\"name\":\"check_in_date\",\"possible_values\":[]},{\"description\":\"Number of days in the reservation\",\"is_categorical\":false,\"name\":\"number_of_days\",\"possible_values\":[]},{\"description\":\"Name of the hotel\",\"is_categorical\":false,\"name\":\"hotel_name\",\"possible_values\":[]},{\"description\":\"Address of the hotel\",\"is_categorical\":false,\"name\":\"street_address\",\"possible_values\":[]},{\"description\":\"Phone number of the hotel\",\"is_categorical\":false,\"name\":\"phone_number\",\"possible_values\":[]},{\"description\":\"Price per night for the reservation\",\"is_categorical\":false,\"name\":\"price_per_night\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nGood. I wish to book a room in a hotel there.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00028-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Flights_3\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"City in which the journey originates\",\"is_categorical\":false,\"name\":\"origin_city\",\"possible_values\":[]},{\"description\":\"City in which the journey ends\",\"is_categorical\":false,\"name\":\"destination_city\",\"possible_values\":[]},{\"description\":\"Number of the airport flying out from\",\"is_categorical\":false,\"name\":\"origin_airport_name\",\"possible_values\":[]},{\"description\":\"Number of the airport flying to\",\"is_categorical\":false,\"name\":\"destination_airport_name\",\"possible_values\":[]},{\"description\":\"Date of departure flight\",\"is_categorical\":false,\"name\":\"departure_date\",\"possible_values\":[]},{\"description\":\"Date of return flight\",\"is_categorical\":false,\"name\":\"return_date\",\"possible_values\":[]},{\"description\":\"Local time of departure of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_departure_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_arrival_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from destination to origin\",\"is_categorical\":false,\"name\":\"inbound_arrival_time\",\"possible_values\":[]},{\"desc",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00032-2-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"departure_date\",\"value\":\"the 10th\"},{\"slot\":\"destination_city\",\"value\":\"portland\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Flights_3\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"City in which the journey originates\",\"is_categorical\":false,\"name\":\"origin_city\",\"possible_values\":[]},{\"description\":\"City in which the journey ends\",\"is_categorical\":false,\"name\":\"destination_city\",\"possible_values\":[]},{\"description\":\"Number of the airport flying out from\",\"is_categorical\":false,\"name\":\"origin_airport_name\",\"possible_values\":[]},{\"description\":\"Number of the airport flying to\",\"is_categorical\":false,\"name\":\"destination_airport_name\",\"possible_values\":[]},{\"description\":\"Date of departure flight\",\"is_categorical\":false,\"name\":\"departure_date\",\"possible_values\":[]},{\"description\":\"Date of return flight\",\"is_categorical\":false,\"name\":\"return_date\",\"possible_values\":[]},{\"description\":\"Local time of departure of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_departure_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_arrival_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from destination to origin\",\"is_categorical\":false,\"name\":\"inbound_arrival_time\",\"possible_values\":[]},{\"desc",
"subject_answer_excerpt": "\n[{\"slot\":\"departure_date\",\"value\":\"the 10th\"},{\"slot\":\"destination_city\",\"value\":\"portland\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00033-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Flights_3\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"City in which the journey originates\",\"is_categorical\":false,\"name\":\"origin_city\",\"possible_values\":[]},{\"description\":\"City in which the journey ends\",\"is_categorical\":false,\"name\":\"destination_city\",\"possible_values\":[]},{\"description\":\"Number of the airport flying out from\",\"is_categorical\":false,\"name\":\"origin_airport_name\",\"possible_values\":[]},{\"description\":\"Number of the airport flying to\",\"is_categorical\":false,\"name\":\"destination_airport_name\",\"possible_values\":[]},{\"description\":\"Date of departure flight\",\"is_categorical\":false,\"name\":\"departure_date\",\"possible_values\":[]},{\"description\":\"Date of return flight\",\"is_categorical\":false,\"name\":\"return_date\",\"possible_values\":[]},{\"description\":\"Local time of departure of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_departure_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from origin to destination\",\"is_categorical\":false,\"name\":\"outbound_arrival_time\",\"possible_values\":[]},{\"description\":\"Local time of arrival of flight from destination to origin\",\"is_categorical\":false,\"name\":\"inbound_arrival_time\",\"possible_values\":[]},{\"desc",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00034-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"11th of this month\"},{\"slot\":\"city\",\"value\":\"london, england\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\n11th of this month in london, england",
"subject_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"11th of this month\"},{\"slot\":\"city\",\"value\":\"london, england\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00035-14-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"ten am\"},{\"slot\":\"pickup_city\",\"value\":\"New York City\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI would like to pick the car up around ten am from New York City.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"ten am\"},{\"slot\":\"pickup_location\",\"value\":\"New York City\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00036-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"day after tomorrow\"},{\"slot\":\"city\",\"value\":\"Paris, France\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI would like to check the weather for the day after tomorrow in Paris, France.",
"subject_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"day after tomorrow\"},{\"slot\":\"city\",\"value\":\"Paris, France\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00037-20-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nWhat's the cost of it?",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00038-12-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"12 in the afternoon\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI'd like to pick up the vehicle near 12 in the afternoon.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"12 in the afternoon\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00039-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"city\",\"value\":\"London, England\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nWhat is the weather in London, England?",
"subject_answer_excerpt": "\n[{\"slot\":\"city\",\"value\":\"London, England\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00040-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"11th of this month\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nHello. I want to see what the weather is like on the 11th of this month.",
"subject_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"11th of this month\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00041-4-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nHow strong will the winds be?",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00043-4-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI would like a one-way flight on Saturday this week.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00044-4-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nOkay thanks, I'd like to get a one-way plane ticket going there. I'm leaving from New York and I need 4 seats.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00045-8-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nThat works. Could you find me a rental car?",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00046-14-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_date\",\"value\":\"March 9th\"},{\"slot\":\"pickup_time\",\"value\":\"half past 6 in the evening\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI will be needing to rent the car from March 9th, and I want to pick it up around half past 6 in the evening.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_date\",\"value\":\"March 9th\"},{\"slot\":\"pickup_time\",\"value\":\"half past 6 in the evening\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00047-16-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI need the car until the 14th of this month.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00048-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"next Wednesday\"},{\"slot\":\"city\",\"value\":\"San Francisco\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nTell me the weather next Wednesday in San Francisco.",
"subject_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"next Wednesday\"},{\"slot\":\"city\",\"value\":\"San Francisco\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00049-4-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_date\",\"value\":\"6th of March\"},{\"slot\":\"pickup_city\",\"value\":\"Seattle, WA\"},{\"slot\":\"dropoff_date\",\"value\":\"11th\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nFine, I'll need a rental car there. What about if I pick it up on the 6th of March in Seattle, WA and turn back on the 11th?",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_date\",\"value\":\"6th of March\"},{\"slot\":\"pickup_city\",\"value\":\"Seattle, WA\"},{\"slot\":\"dropoff_date\",\"value\":\"11th\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00050-6-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nAlright, fine. I'd like to verify for one-way flights headed there.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00051-10-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nAround evening 5 thanks.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00052-16-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nThe 13th of this month.",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"incorrect\"}",
"status": "completed"
}
],
"parse": {
"rating": "incorrect",
"status": "ok",
"value": "0"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00053-16-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"09:30\"},{\"slot\":\"pickup_date\",\"value\":\"March 6th\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\n09:30 and March 6th.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"09:30\"},{\"slot\":\"pickup_date\",\"value\":\"March 6th\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00054-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"7th of this month\"},{\"slot\":\"city\",\"value\":\"Cotati\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nWhat's the weather for the 7th of this month in Cotati?",
"subject_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"7th of this month\"},{\"slot\":\"city\",\"value\":\"Cotati\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00055-18-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"12:15\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nAround afternoon 12:15 I want to be the pick up.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"12:15\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00056-10-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"afternoon 3:45\"},{\"slot\":\"pickup_location\",\"value\":\"Las Vegas\"},{\"slot\":\"pickup_date\",\"value\":\"March 3rd\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nThat's fine. I'm going to need a rental car. I'd like to pick it up afternoon 3:45 in Las Vegas on March 3rd.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"afternoon 3:45\"},{\"slot\":\"pickup_location\",\"value\":\"Las Vegas\"},{\"slot\":\"pickup_date\",\"value\":\"March 3rd\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00057-2-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"city\",\"value\":\"Portland\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nIn Portland will be okay.",
"subject_answer_excerpt": "\n[{\"slot\":\"city\",\"value\":\"Portland\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00058-10-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_date\",\"value\":\"March 1st\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI will get it on March 1st.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_date\",\"value\":\"March 1st\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00059-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"city\",\"value\":\"los altos\"},{\"slot\":\"date\",\"value\":\"march 11th\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nlos altos march 11th",
"subject_answer_excerpt": "\n[{\"slot\":\"city\",\"value\":\"los altos\"},{\"slot\":\"date\",\"value\":\"march 11th\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00060-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"3rd of March\"},{\"slot\":\"city\",\"value\":\"San Lorenzo\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nCheck the forecast on the 3rd of March in San Lorenzo.",
"subject_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"3rd of March\"},{\"slot\":\"city\",\"value\":\"San Lorenzo\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00061-14-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_location\",\"value\":\"Long Beach, CA\"},{\"slot\":\"pickup_time\",\"value\":\"one pm\"},{\"slot\":\"dropoff_date\",\"value\":\"11th of March\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI want to pick it up in Long Beach, CA around one pm and I need it till 11th of March.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_location\",\"value\":\"Long Beach, CA\"},{\"slot\":\"pickup_time\",\"value\":\"one pm\"},{\"slot\":\"dropoff_date\",\"value\":\"11th of March\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00062-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"the 11th\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI must verify the weather on the 11th.",
"subject_answer_excerpt": "\n[{\"slot\":\"date\",\"value\":\"the 11th\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00063-2-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI see, thank you!",
"subject_answer_excerpt": "\n[]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00064-10-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"3 pm\"},{\"slot\":\"pickup_location\",\"value\":\"SD\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI need to get it at 3 pm from SD.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"3 pm\"},{\"slot\":\"pickup_location\",\"value\":\"SD\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00065-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"city\",\"value\":\"Paris\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI need to see what the weather is like in Paris.",
"subject_answer_excerpt": "\n[{\"slot\":\"city\",\"value\":\"Paris\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00066-0-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"city\",\"value\":\"Seattle, WA\"},{\"slot\":\"date\",\"value\":\"2nd of this month\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: Weather_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"The possibility of rain or snow in percentage\",\"is_categorical\":false,\"name\":\"precipitation\",\"possible_values\":[]},{\"description\":\"Percentage humidity\",\"is_categorical\":false,\"name\":\"humidity\",\"possible_values\":[]},{\"description\":\"Wind speed in miles per hour\",\"is_categorical\":false,\"name\":\"wind\",\"possible_values\":[]},{\"description\":\"Temperature in Fahrenheit\",\"is_categorical\":false,\"name\":\"temperature\",\"possible_values\":[]},{\"description\":\"Name of the city\",\"is_categorical\":false,\"name\":\"city\",\"possible_values\":[]},{\"description\":\"Date for the weather\",\"is_categorical\":false,\"name\":\"date\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nI need to know the expected weather in Seattle, WA on the 2nd of this month.",
"subject_answer_excerpt": "\n[{\"slot\":\"city\",\"value\":\"Seattle, WA\"},{\"slot\":\"date\",\"value\":\"2nd of this month\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00067-8-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"afternoon 3\"},{\"slot\":\"pickup_city\",\"value\":\"San Francisco\"},{\"slot\":\"dropoff_date\",\"value\":\"4th of March\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nfine, now could you assist me to get a rental car. I will collect it at afternoon 3 in San Francisco nad return it on 4th of March.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_time\",\"value\":\"afternoon 3\"},{\"slot\":\"pickup_city\",\"value\":\"San Francisco\"},{\"slot\":\"dropoff_date\",\"value\":\"4th of March\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}sgd-dev-13_00069-14-0-span__main_slot_value_01
{
"baseline_answer_excerpt": "\n[{\"slot\":\"pickup_location\",\"value\":\"Paris, France\"},{\"slot\":\"car_name\",\"value\":\"small car\"}]",
"input_excerpt": "Extract directly mentioned noncategorical slot values for the named service from the CURRENT USER UTTERANCE only. Return a raw JSON array, without Markdown fences or explanation. Each object must have exactly keys \"slot\" and \"value\". Use an exact allowed schema slot name and a nonempty verbatim substring of the unchanged current utterance as its value. Do not normalize, paraphrase, infer omitted values, copy earlier-turn state, or include requested slots without mentioned values. Do not emit character offsets. Return [] if no directly mentioned noncategorical values occur. Each distinct slot/value pair may appear only once, even when that value occurs more than once in the utterance. Pair order does not matter. Different slot names may use the same verbatim value.\nSERVICE: RentalCars_1\nNONCATEGORICAL SLOT SCHEMA:\n[{\"description\":\"Model name of rental car\",\"is_categorical\":false,\"name\":\"car_name\",\"possible_values\":[]},{\"description\":\"Location of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_location\",\"possible_values\":[]},{\"description\":\"Date of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_date\",\"possible_values\":[]},{\"description\":\"Time of rental car pickup\",\"is_categorical\":false,\"name\":\"pickup_time\",\"possible_values\":[]},{\"description\":\"City to pick up the rental car\",\"is_categorical\":false,\"name\":\"pickup_city\",\"possible_values\":[]},{\"description\":\"Date of rental car drop-off\",\"is_categorical\":false,\"name\":\"dropoff_date\",\"possible_values\":[]},{\"description\":\"Total price of car rental\",\"is_categorical\":false,\"name\":\"total_price\",\"possible_values\":[]}]\nCURRENT USER UTTERANCE:\nCan you suggest some other car, that I can pick up from Paris, France. I am interested in having a small car.",
"subject_answer_excerpt": "\n[{\"slot\":\"pickup_location\",\"value\":\"Paris, France\"},{\"slot\":\"car_name\",\"value\":\"small car\"}]",
"trials": [
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 1,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 2,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "baseline",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"partially_correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "partially_correct",
"status": "ok",
"value": "0.5"
},
"repetition": 1,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 2,
"side": "subject",
"status": "complete"
},
{
"attempts": [
{
"error": null,
"resolved_model": "gpt-5.6-luna",
"response_excerpt": "{\"rating\":\"correct\"}",
"status": "completed"
}
],
"parse": {
"rating": "correct",
"status": "ok",
"value": "1"
},
"repetition": 3,
"side": "subject",
"status": "complete"
}
]
}Exact comparison data
{
"assumptions": [
"Declared units are independent; distributions may differ between units.",
"Scores remain within the declared rating scale under the frozen judge protocol.",
"Cases, answers, unit grouping, schedule, and decision policy are fixed before judging.",
"Units have equal weight; cases within units and repetitions within cases have equal weight.",
"Repetitions and cases within a unit do not increase the number of independent units.",
"The declared comparison family includes both published intervals and all enclosing claims."
],
"captured_context": {
"baseline": {},
"changes": [
"Model-key comparison is unavailable because recorded values are missing or mixed.",
"Prompt comparison unavailable in this report projection: no complete, uniquely paired supported message view is available."
],
"context": {
"Baseline capture role": "Unavailable in recorded context",
"Baseline dataset": "sgd",
"Baseline effective message roles": "Unavailable in recorded context",
"Baseline evaluator": "K2 frozen answer reference 1",
"Baseline model revision": "Unavailable in recorded context",
"Baseline records": "1,288",
"Baseline workflow": "extraction",
"Evaluation mode": "Captured evaluator outputs",
"Subject capture role": "Unavailable in recorded context",
"Subject dataset": "sgd",
"Subject effective message roles": "Unavailable in recorded context",
"Subject evaluator": "K2 frozen answer reference 1",
"Subject model revision": "Unavailable in recorded context",
"Subject records": "1,288",
"Subject workflow": "extraction"
},
"subject": {}
},
"effect_interval": {
"alpha": "0.05",
"comparisons": 4,
"lower": "-0.079456562983261",
"mean": "0.009316770186335",
"method": "fixed-benchmark-hoeffding-v1",
"unit_count": 1288,
"upper": "0.098090103355932"
},
"method": "fixed-benchmark-hoeffding-v1",
"policy": {
"allowed_degradation": "0.1",
"alpha": "0.05",
"comparison_family_size": 4,
"decision_role": "required",
"direction": "higher",
"format": "invarlock/judge-analysis-policy-v1",
"maximum_interval_width": "0.18",
"method": "fixed-benchmark-hoeffding-v1",
"metric_name": "extraction-judge-quality",
"minimum_units": 1288,
"plan_sha256": "9375d6a3bfba9089713a49831d7be52445f2efd4c62cfeec0009242df500b8a8",
"subject_bound": "0.6"
},
"subject_interval": {
"alpha": "0.05",
"comparisons": 4,
"lower": "0.859469441075657",
"mean": "0.903856107660455",
"method": "fixed-benchmark-hoeffding-v1",
"unit_count": 1288,
"upper": "0.948242774245254"
}
}