Triple
T2589546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Law Center Campus |
E58085
|
entity |
| Predicate | primaryAcademicDiscipline |
P778
|
FINISHED |
| Object | law |
—
|
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: law | Statement: [Law Center Campus, primaryAcademicDiscipline, law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryAcademicDiscipline Context triple: [Law Center Campus, primaryAcademicDiscipline, law]
-
A.
academicFocus
chosen
Indicates the primary field of study, discipline, or subject area that an entity concentrates on academically.
-
B.
subDisciplineOf
Indicates that one discipline is a more specialized or narrower field within another, broader discipline.
-
C.
hasSubjectOfStudy
Indicates that an entity (such as a person or organization) focuses on, researches, or specializes in a particular field or topic of study.
-
D.
academicType
Indicates the specific academic category or classification associated with an entity (such as a work, program, or role).
-
E.
partOfStudy
Indicates that something is a component, segment, or subset within a larger study or research project.
- F. None of above.
Provenance (3 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_69ab4ac019c8819094add11c46706e32 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd3feb45c81909369a49c3990294a |
completed | March 7, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_69abd0d19308819089ee942513d567a4 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:49 p.m.