Triple
T9861046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mandel Legal Aid Clinic |
E239710
|
entity |
| Predicate | legalStatusOfStudentWork |
P90941
|
FINISHED |
| Object | performed under attorney supervision |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: performed under attorney supervision | Statement: [Mandel Legal Aid Clinic, legalStatusOfStudentWork, performed under attorney supervision]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalStatusOfStudentWork Context triple: [Mandel Legal Aid Clinic, legalStatusOfStudentWork, performed under attorney supervision]
-
A.
academicStatus
Indicates the educational or scholarly standing or level an entity holds within an academic context.
-
B.
eligibleWork
Indicates that a particular work satisfies the necessary conditions or criteria to qualify for a specified status, benefit, or consideration.
-
C.
typicalStudentStatus
Indicates the usual or standard enrollment or academic standing that a student typically holds within an educational context.
-
D.
usedLegalStatus
Indicates that one entity applies or relies on the legal status or classification of another entity in a given context.
-
E.
juridicalSchoolStatus
Indicates the legal or institutional status assigned to a school within a given jurisdiction or educational system.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca84e6493081909cf58c8d42ea856b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3b6aa108190978f1c0cdc0f45a0 |
completed | April 2, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69cd1d7621d48190aa6a6f34399514b0 |
completed | April 1, 2026, 1:28 p.m. |
| PDg | Predicate description generation | batch_69cd3581a9688190a00cef4c3eebb0ae |
completed | April 1, 2026, 3:10 p.m. |
Created at: March 30, 2026, 8:35 p.m.