Triple

T5713281
Position Surface form Disambiguated ID Type / Status
Subject The Mirror Has Two Faces E125960 entity
Predicate leadActor P1507 FINISHED
Object Jeff Bridges E101127 NE 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: Jeff Bridges | Statement: [The Mirror Has Two Faces, leadActor, Jeff Bridges]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jeff Bridges
Context triple: [The Mirror Has Two Faces, leadActor, Jeff Bridges]
  • A. Jeff Bridges chosen
    Jeff Bridges is an acclaimed American actor known for his versatile performances in films such as "The Big Lebowski," "Crazy Heart," and "True Grit."
  • B. Gene Hackman
    Gene Hackman is an acclaimed American actor known for his powerful, versatile performances in films such as "The French Connection," "The Conversation," and "Unforgiven."
  • C. Bill Paxton
    Bill Paxton was an American actor and filmmaker known for his versatile roles in films such as "Aliens," "Twister," "Titanic," and "Apollo 13."
  • D. Ned Beatty
    Ned Beatty was an acclaimed American character actor known for his powerful supporting roles in films such as "Deliverance," "Network," and "Superman."
  • E. Kurt Russell
    Kurt Russell is an American actor known for his versatile performances in films ranging from action and science fiction to drama and comedy, including iconic roles in movies like "Escape from New York," "The Thing," and "Tombstone."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69c0082d6fe48190b777fb383769e5c8 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c024b5205c8190aaab291a6e485ec1 completed March 22, 2026, 5:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0b0b3412c8190a4b97863e060e928 completed March 23, 2026, 3:17 a.m.
Created at: March 22, 2026, 3:46 p.m.