Triple
T2459393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Thomas Crown Affair (1968 film) |
E54494
|
entity |
| Predicate | femaleLeadOccupation |
P21567
|
FINISHED |
| Object | insurance investigator |
—
|
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: insurance investigator | Statement: [The Thomas Crown Affair (1968 film), femaleLeadOccupation, insurance investigator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleLeadOccupation Context triple: [The Thomas Crown Affair (1968 film), femaleLeadOccupation, insurance investigator]
-
A.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
B.
hasLeadCharacterGender
Indicates that the primary or lead character in a work has a specified gender.
-
C.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
D.
representedOccupation
Indicates that one entity has served as an official or formal representative of another entity’s occupation or professional role.
-
E.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
- 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_69ab49dee84c819096b50a0049c347ac |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd49c5aa081909ab4f726a458b77f |
completed | March 7, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69abd0b199488190aa381b36593ae1ac |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:44 p.m.