Triple

T22899620
Position Surface form Disambiguated ID Type / Status
Subject Catch a Fire E568272 entity
Predicate starredActor P5563 FINISHED
Object Tim Robbins NE NERFINISHED

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: Tim Robbins | Statement: [Catch a Fire, starredActor, Tim Robbins]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tim Robbins
Context triple: [Catch a Fire, starredActor, Tim Robbins]
  • A. Tim Robbins chosen
    Tim Robbins is an American actor, director, and producer best known for his roles in films such as The Shawshank Redemption, Mystic River, and Bull Durham.
  • B. William Hurt
    William Hurt was an acclaimed American actor known for his intense, introspective performances in films such as "Kiss of the Spider Woman," "Broadcast News," and "The Big Chill."
  • C. James Woods
    James Woods is an American actor known for his intense performances in film and television, including acclaimed roles in movies such as "Salvador," "Videodrome," and "Casino."
  • D. Peter Strauss
    Peter Strauss is an American actor best known for his work in television miniseries and films, including prominent roles in dramas throughout the 1970s and 1980s.
  • E. Jeff Bridges
    Jeff Bridges is an acclaimed American actor known for his versatile performances in films such as "The Big Lebowski," "Crazy Heart," and "True Grit."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2458c23ec81908fa2570692c6614f completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f180155b1c8190a83eb6ec45387a1a completed April 29, 2026, 3:50 a.m.
Created at: April 17, 2026, 3:41 p.m.