Triple
T29313897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York City legal community |
E743324
|
entity |
| Predicate | includesPracticeArea |
P12790
|
FINISHED |
| Object | corporate 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: corporate law | Statement: [New York City legal community, includesPracticeArea, corporate law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesPracticeArea Context triple: [New York City legal community, includesPracticeArea, corporate law]
-
A.
regionOfPractice
Indicates the geographic area or jurisdiction in which an entity regularly conducts its professional activities or services.
-
B.
practicedLawIn
Indicates that a person engaged in the professional practice of law within a specified jurisdiction or location.
-
C.
practicedInField
Indicates that an entity has engaged in practical work, training, or professional activity within a specified field or domain.
-
D.
hasLegalSubjectArea
Indicates that something (such as a document, case, or rule) pertains to or is classified under a particular area of law.
-
E.
practiceType
chosen
Indicates the specific kind or category of practice associated with an entity or activity.
- 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_69f0912502c8819087d9e8398ee991a8 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69ffada24d188190a576a02dc280a7fb |
completed | May 9, 2026, 9:56 p.m. |
| PD | Predicate disambiguation | batch_69ffad46d6ac819081772f408b1389d5 |
completed | May 9, 2026, 9:55 p.m. |
Created at: April 28, 2026, 1:18 p.m.