Triple
T18508455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tommy Lee Jones as Gene McClary |
E452262
|
entity |
| Predicate | belongsToFilmCategory |
P131943
|
FINISHED |
| Object | corporate drama |
—
|
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: corporate drama | Statement: [Tommy Lee Jones as Gene McClary, belongsToFilmCategory, corporate drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToFilmCategory Context triple: [Tommy Lee Jones as Gene McClary, belongsToFilmCategory, corporate drama]
-
A.
inheritedByInFilm
Indicates that a character, role, or attribute is passed on or taken over by another character within the narrative of a film.
-
B.
associatedWithFilmCharacterType
Indicates that an entity has an association or connection with a particular type or category of film character.
-
C.
ownedByInFilm
Indicates that one entity is portrayed as being owned or possessed by another entity within the context of a specific film.
-
D.
hasTypeOfUseInFilm
Indicates that something is associated with a specific manner or category of use within the context of a film.
-
E.
filmFestivalCategory
Indicates the specific category or section of a film festival in which a film is entered, screened, or competes.
- 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_69d8d386df84819092355ebb260d848e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53344c6b081908e780ed5c815a766 |
completed | April 19, 2026, 7:55 p.m. |
| PD | Predicate disambiguation | batch_69e469dbf5208190b6fc49e02a087f54 |
completed | April 19, 2026, 5:36 a.m. |
| PDg | Predicate description generation | batch_69e46d2b93bc8190a6070018d7046547 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 11:36 a.m.