Triple
T38140729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Independent Film Company |
E952470
|
entity |
| Predicate | typeOfFilmHandled |
P136433
|
FINISHED |
| Object | independent 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 films | Statement: [Independent Film Company, typeOfFilmHandled, independent films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfFilmHandled Context triple: [Independent Film Company, typeOfFilmHandled, independent films]
-
A.
filmType
Indicates the specific category or genre that a film belongs to.
-
B.
filmTypeContext
Indicates the contextual relationship between a film and its type or category within a specific classification or usage setting.
-
C.
workFilmType
chosen
Indicates the type or category of film associated with a particular work.
-
D.
cinemaType
Indicates the specific category or kind of cinema associated with an entity (e.g., multiplex, art house, drive-in).
-
E.
recognizesFilmGenre
Indicates that an entity identifies or acknowledges the film genre to which a particular movie belongs.
- 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_69f76f09a7148190a4b91c0bacdc127a |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:21 p.m.