Triple
T33748758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harry O |
E864774
|
entity |
| Predicate | leadCharacterFormerOccupation |
P35945
|
FINISHED |
| Object | San Diego 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: San Diego police officer | Statement: [Harry O, leadCharacterFormerOccupation, San Diego police officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadCharacterFormerOccupation Context triple: [Harry O, leadCharacterFormerOccupation, San Diego police officer]
-
A.
characterFormerOccupation
chosen
Indicates that a character previously held a specific occupation but no longer does.
-
B.
leadActorOccupation
Indicates that the occupation specified is the primary professional role of the lead actor in a given work or context.
-
C.
earlierOccupation
Indicates that one occupation held by an entity occurred before another occupation in that entity’s work history.
-
D.
economicRolePast
Indicates that an entity previously held a specific economic function, position, or role in the past.
-
E.
leadCharacterSecondaryOccupation
Indicates that the lead character has a secondary or additional occupation beyond their primary role.
- 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_69f3498c35f881909df279ae4270f831 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fb8e24e0819085dac90f2953df45 |
completed | May 3, 2026, 7:38 a.m. |
| PD | Predicate disambiguation | batch_69f6f96ea0c08190902a3d0e1f263e8c |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:45 a.m.