Triple
T34249160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Producers Sales Organization |
E878689
|
entity |
| Predicate | handledFilmType |
P77497
|
FINISHED |
| Object | independent feature 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 feature films | Statement: [The Producers Sales Organization, handledFilmType, independent feature films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: handledFilmType Context triple: [The Producers Sales Organization, handledFilmType, independent feature films]
-
A.
filmTypeContext
chosen
Indicates the contextual relationship between a film and its type or category within a specific classification or usage setting.
-
B.
filmType
Indicates the specific category or genre that a film belongs to.
-
C.
backedFilmType
Indicates that an entity has provided financial or production support specifically for a film of a given type or category.
-
D.
hasTypeOfUseInFilm
Indicates that something is associated with a specific manner or category of use within the context of a film.
-
E.
workFilmType
Indicates the type or category of film associated with a particular work.
- 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_69f349b3618481909df955b063f305b2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71fb1ab3881908e2f7c0e6f23db49 |
completed | May 3, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f71cc6397881909aaad37a9daa8a7e |
completed | May 3, 2026, 10 a.m. |
Created at: May 1, 2026, 1:56 a.m.