Triple

T28664674
Position Surface form Disambiguated ID Type / Status
Subject Zihuatanejo (in film ending) E725555 entity
Predicate cinematicRole P165190 FINISHED
Object closing image of the 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: closing image of the film | Statement: [Zihuatanejo (in film ending), cinematicRole, closing image of the film]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: cinematicRole
Context triple: [Zihuatanejo (in film ending), cinematicRole, closing image of the film]
  • A. creditedRoleOf
    Indicates that a particular role or position is formally acknowledged as being held or performed by a specific entity in a credit or attribution context.
  • B. 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.
  • C. hasFilmographyType
    Indicates the type or category of film-related work associated with an entity (e.g., actor, director, producer) within its filmography.
  • D. cultFilmAppearance
    Indicates that an entity appears in, or is featured as part of, a cult film.
  • E. cinematicSignificance
    Indicates the degree to which something holds notable importance, influence, or impact within the realm of cinema or film history.
  • 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_69f01d85be388190b669a0e401e2f2c4 completed April 28, 2026, 2:37 a.m.
NER Named-entity recognition batch_69f65705a3048190a3728b695ba2ae65 completed May 2, 2026, 7:56 p.m.
PD Predicate disambiguation batch_69f651ac855481908e30c3b345d31356 completed May 2, 2026, 7:34 p.m.
PDg Predicate description generation batch_69f6562ef4e4819082ce6abd41b74dc5 completed May 2, 2026, 7:53 p.m.
Created at: April 28, 2026, 5 a.m.