Triple
T30362419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yennai Arindhaal |
E772322
|
entity |
| Predicate | leadRoleOccupation |
P110410
|
FINISHED |
| Object | police officer |
—
|
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: police officer | Statement: [Yennai Arindhaal, leadRoleOccupation, police officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadRoleOccupation Context triple: [Yennai Arindhaal, leadRoleOccupation, police officer]
-
A.
leadActorOccupation
chosen
Indicates that the occupation specified is the primary professional role of the lead actor in a given work or context.
-
B.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
C.
otherProtagonistOccupation
Indicates that another main character in the narrative has a specific occupation or job role.
-
D.
leadOccupation
Indicates that one entity serves as the primary or main occupation held by another entity.
-
E.
roleDuringOccupation
Indicates the specific role or position an entity held during a particular occupation or period of control.
- 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_69f2248d71408190aec0d5c2001b1cff |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68243b5d8819092d8a0a1261f5fb2 |
completed | May 2, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 7:58 p.m.