Triple

T6444138
Position Surface form Disambiguated ID Type / Status
Subject Grant Heslov E138298 entity
Predicate partnerInBusiness P282 FINISHED
Object George Clooney E11669 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: George Clooney | Statement: [Grant Heslov, partnerInBusiness, George Clooney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: George Clooney
Context triple: [Grant Heslov, partnerInBusiness, George Clooney]
  • A. George Clooney chosen
    George Clooney is an American actor, filmmaker, and activist renowned for his work in film and television as well as his humanitarian and political advocacy.
  • B. Matt Damon
    Matt Damon is an American actor, producer, and screenwriter known for his versatile performances in films such as Good Will Hunting, the Bourne series, and The Martian.
  • C. Tom Hanks
    Tom Hanks is an acclaimed American actor and filmmaker renowned for his versatile performances in films such as "Forrest Gump," "Saving Private Ryan," and "Cast Away."
  • D. Brad Pitt
    Brad Pitt is an American actor and film producer renowned for his leading roles in major Hollywood films and for winning multiple Academy Awards.
  • E. Danny Huston
    Danny Huston is an American actor and director known for his character roles in films such as "The Constant Gardener," "X-Men Origins: Wolverine," and "Wonder Woman."
  • 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_69c008aa61ac8190bc96715ed79fe2d8 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0698c17ec81909f6bbcbe636a67fd completed March 22, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69c64bc718dc8190b186d09a17562d26 completed March 27, 2026, 9:20 a.m.
Created at: March 22, 2026, 4:46 p.m.