Triple
T800236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maurice Podoloff |
E17112
|
entity |
| Predicate | legalOccupation |
P5610
|
FINISHED |
| Object | attorney |
—
|
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: attorney | Statement: [Maurice Podoloff, legalOccupation, attorney]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalOccupation Context triple: [Maurice Podoloff, legalOccupation, attorney]
-
A.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
B.
traditionalOccupations
Indicates that an entity is associated with occupations or jobs that are customary, long-established, or culturally traditional within a particular community or context.
-
C.
representedOccupation
Indicates that one entity has served as an official or formal representative of another entity’s occupation or professional role.
-
D.
legalProfessionRole
chosen
Indicates that one entity holds or performs a specific professional role within the legal domain in relation to another entity or context.
-
E.
sponsorOccupation
Indicates that one entity serves as the occupation or professional role of a sponsor associated with another entity.
- 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a7cb26dc8190bdd3a278b8695873 |
completed | March 1, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69a4a5133bf88190a613e96d1f7cffa7 |
completed | March 1, 2026, 8:44 p.m. |
Created at: March 1, 2026, 7:38 p.m.