Triple
T25713156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Only Game in Town |
E644786
|
entity |
| Predicate | hasWarrenBeattyFilmographyEntry |
P163697
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [The Only Game in Town, hasWarrenBeattyFilmographyEntry, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWarrenBeattyFilmographyEntry Context triple: [The Only Game in Town, hasWarrenBeattyFilmographyEntry, yes]
-
A.
hasHumanCast
Indicates that a work or production features human performers as part of its cast.
-
B.
hasWorkedOnFilmBy
Indicates that one entity has worked on a film that was created, directed, or otherwise authored by another entity.
-
C.
featuredInFilmBy
Indicates that an entity is prominently included or showcased within a film that is created, directed, or produced by a specified person or organization.
-
D.
hasFilmographyType
Indicates the type or category of film-related work associated with an entity (e.g., actor, director, producer) within its filmography.
-
E.
hasDirectorStar
Indicates that a person both directed and starred in the same work (e.g., film, show, or production).
- 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_69e77e83c8ec8190bf52fcdac4838984 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6397b64f881909d811225e57aac5e |
completed | May 2, 2026, 5:50 p.m. |
| PD | Predicate disambiguation | batch_69f63706b6008190993577193c85ff50 |
completed | May 2, 2026, 5:40 p.m. |
| PDg | Predicate description generation | batch_69f638d029148190877c103f0eeaf147 |
completed | May 2, 2026, 5:48 p.m. |
Created at: April 21, 2026, 9:21 p.m.