Triple
T14310640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United International Pictures |
E354816
|
entity |
| Predicate | roleInFilmIndustry |
P55759
|
FINISHED |
| Object | international distributor |
—
|
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: international distributor | Statement: [United International Pictures, roleInFilmIndustry, international distributor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInFilmIndustry Context triple: [United International Pictures, roleInFilmIndustry, international distributor]
-
A.
roleInFilmEcosystem
chosen
Indicates the specific function or position an entity holds within the broader network of activities, stakeholders, and processes that make up the film ecosystem.
-
B.
roleInCinerama
Indicates that an entity has a role or participation in a Cinerama film or production.
-
C.
placementInFilm
Indicates the specific position or occurrence of something within the sequence or structure of a film.
-
D.
creativeRole
Indicates that an entity holds a specific creative function or responsibility in relation to another entity, such as a work or project.
-
E.
givenNameInFilm
Indicates that a person is referred to by a particular given (first) name within the context of a specific film.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de85b386d0819087d14f3ce84a1997 |
completed | April 14, 2026, 6:21 p.m. |
| PD | Predicate disambiguation | batch_69de2a8f81f08190af737e1654847aa6 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:12 a.m.