Triple
T33913388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York City (fictional setting of film) |
E869377
|
entity |
| Predicate | hasCinematicRole |
P165190
|
FINISHED |
| Object | establishing shots of cityscape |
—
|
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: establishing shots of cityscape | Statement: [New York City (fictional setting of film), hasCinematicRole, establishing shots of cityscape]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCinematicRole Context triple: [New York City (fictional setting of film), hasCinematicRole, establishing shots of cityscape]
-
A.
cinematicRole
chosen
Indicates that one entity performs or holds a specific role or character part within a cinematic work associated with another entity.
-
B.
hasFilmographyType
Indicates the type or category of film-related work associated with an entity (e.g., actor, director, producer) within its filmography.
-
C.
featuredInFilmBy
Indicates that an entity is prominently included or showcased within a film that is created, directed, or produced by a specified person or organization.
-
D.
hasFilmCareer
Indicates that an entity has been professionally involved in the film industry as a career.
-
E.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
- 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_69f3499869bc8190b6c33a81686af226 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8cec6d48190bebfa884b2f938c0 |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 1, 2026, 1:48 a.m.