Triple
T10701691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Saul Zaentz Company |
E252291
|
entity |
| Predicate | notableFilmGenre |
P46117
|
FINISHED |
| Object | literary adaptations |
—
|
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: literary adaptations | Statement: [The Saul Zaentz Company, notableFilmGenre, literary adaptations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableFilmGenre Context triple: [The Saul Zaentz Company, notableFilmGenre, literary adaptations]
-
A.
sourceFilmGenre
Indicates that a film is classified as belonging to a particular genre.
-
B.
keyGenreFilm
Indicates that a particular genre is the primary or defining genre associated with a given film.
-
C.
notableGenreAdaptation
Indicates that a work is a significant adaptation of another work into a different genre.
-
D.
distributedFilmGenre
Indicates that an entity (such as a company or distributor) distributed a film belonging to a particular genre.
-
E.
notableProductionType
chosen
Indicates that the subject is particularly known for producing or creating instances of the specified type.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd8c835c8190bf1a67ee94195926 |
completed | April 9, 2026, 1:14 a.m. |
| PD | Predicate disambiguation | batch_69d6dd8cc0788190b4c02a772e4b58b3 |
completed | April 8, 2026, 10:58 p.m. |
Created at: April 8, 2026, 9:12 p.m.