Triple
T34604495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | O. J. Berman |
E888557
|
entity |
| Predicate | triedToTurnIntoMovieStar |
P179687
|
FINISHED |
| Object | Holly Golightly |
—
|
NE NERFINISHED |
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: Holly Golightly | Statement: [O. J. Berman, triedToTurnIntoMovieStar, Holly Golightly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: triedToTurnIntoMovieStar Context triple: [O. J. Berman, triedToTurnIntoMovieStar, Holly Golightly]
-
A.
startedActingCareer
Indicates that an entity began their professional work or involvement in acting at a specific time or event.
-
B.
startedCareerAsChildActor
Indicates that a person began their professional career in acting during childhood.
-
C.
hasFilmCareer
Indicates that an entity has been professionally involved in the film industry as a career.
-
D.
helpedPropelToMainstreamFame
Indicates that one entity significantly contributed to another entity’s rise to widespread public recognition or mainstream popularity.
-
E.
beganModelingCareer
Indicates that an entity started or initiated their professional modeling career at a particular time or under certain circumstances.
- F. None of above. chosen
Provenance (4 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_69f349d489d48190ba30e7d97c6f5ef9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7238172748190b8cd340ad1f4ba80 |
completed | May 3, 2026, 10:29 a.m. |
| PD | Predicate disambiguation | batch_69f72155c48881909bd40b9aa3febd5a |
completed | May 3, 2026, 10:20 a.m. |
| PDg | Predicate description generation | batch_69f72349f1108190b6a06758ab2f40bb |
completed | May 3, 2026, 10:28 a.m. |
Created at: May 1, 2026, 2:03 a.m.