Triple

T4614638
Position Surface form Disambiguated ID Type / Status
Subject Sexiest Man Alive E100837 entity
Predicate hasNotableRecipient P108 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: [Sexiest Man Alive, hasNotableRecipient, George Clooney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: George Clooney
Context triple: [Sexiest Man Alive, hasNotableRecipient, 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_69bd43cf363c819087fd5ab441b4a3f4 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd59c3d9ec8190a50ef03627dc351d completed March 20, 2026, 2:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfa895b7481909e54cfa56a54c8dc completed March 21, 2026, 1:55 a.m.
Created at: March 20, 2026, 1:12 p.m.