Triple
T13473986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USA Films |
E318201
|
entity |
| Predicate | filmTypeDistributed |
P73772
|
FINISHED |
| Object | independent drama films |
—
|
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: independent drama films | Statement: [USA Films, filmTypeDistributed, independent drama films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmTypeDistributed Context triple: [USA Films, filmTypeDistributed, independent drama films]
-
A.
distributedFilmType
Indicates that a film was distributed in a particular format or category of distribution.
-
B.
filmType
Indicates the specific category or genre that a film belongs to.
-
C.
distributedFilm
Indicates that one entity served as the distributor responsible for releasing or circulating a particular film.
-
D.
filmTypeContext
Indicates the contextual relationship between a film and its type or category within a specific classification or usage setting.
-
E.
distributedFilmGenre
chosen
Indicates that an entity (such as a company or distributor) distributed a film belonging to a particular genre.
- 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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf2447bc81908baf1f4b55095144 |
completed | April 12, 2026, 2:41 p.m. |
| PD | Predicate disambiguation | batch_69dbadfddefc81909ef7fde23b181b5c |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:42 p.m.