Triple
T35334697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Catch |
E1020419
|
entity |
| Predicate | hasFilmFootage |
P22752
|
FINISHED |
| Object | black-and-white game broadcast 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 game broadcast film | Statement: [The Catch, hasFilmFootage, black-and-white game broadcast film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmFootage Context triple: [The Catch, hasFilmFootage, black-and-white game broadcast film]
-
A.
hasLiveActionFilm
Indicates that a subject has a corresponding live-action film adaptation or representation.
-
B.
hasFilmExperience
Indicates that an entity has prior involvement or participation in film-related activities or productions.
-
C.
usesArchivalFootageFrom
Indicates that one entity incorporates or includes archival footage originating from another entity.
-
D.
hasBehindTheScenesFilm
Indicates that one work includes or is associated with a behind-the-scenes film documenting its creation or production process.
-
E.
usesFootageType
chosen
Indicates that one entity employs or incorporates a particular type or category of footage in its content or production.
- 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_69f76debb4e08190be52d89b8af2392d |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ffc89596d08190b97bd60b45c7f9c0 |
completed | May 9, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69ffc81ba5dc8190ae94d44e2284948f |
completed | May 9, 2026, 11:49 p.m. |
Created at: May 3, 2026, 4:03 p.m.