Triple
T26299626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mickey McGuire comedy film shorts |
E661517
|
entity |
| Predicate | cinemaType |
P160222
|
FINISHED |
| Object | black-and-white film |
—
|
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: black-and-white film | Statement: [Mickey McGuire comedy film shorts, cinemaType, black-and-white film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cinemaType Context triple: [Mickey McGuire comedy film shorts, cinemaType, black-and-white film]
-
A.
cinemaCategory
Indicates the classification or genre category assigned to a cinema or film.
-
B.
cinemaOf
Indicates a relationship where a cinema is associated with, belongs to, or is located within a particular place, organization, or context.
-
C.
theaterType
Indicates the specific kind or category of theater associated with an entity (e.g., cinema, opera house, drama theater).
-
D.
filmSelection
Indicates the act or result of choosing a particular film from a set of available options.
-
E.
theatricalReleaseType
Indicates the manner or category of a work’s release in theaters, such as wide, limited, or special event distribution.
- F. None of above. chosen
Provenance (4 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_69ee812cd48c81908054068f545f0526 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f60eb1ac9c8190a0b9193f4108dfbe |
completed | May 2, 2026, 2:48 p.m. |
| PD | Predicate disambiguation | batch_69f5f7ff548c8190a23e98c5e66e0bc7 |
completed | May 2, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69f5ffc6268c8190b63f6360ebadab73 |
completed | May 2, 2026, 1:44 p.m. |
Created at: April 26, 2026, 10:14 p.m.