Triple

T13849565
Position Surface form Disambiguated ID Type / Status
Subject Miguel Ferrer E332897 entity
Predicate relative P37 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: [Miguel Ferrer, relative, George Clooney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: George Clooney
Context triple: [Miguel Ferrer, relative, 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. Anthony Howard Goldwyn
    Anthony Howard Goldwyn is an American actor, director, and producer best known for his roles in films like "Ghost" and the TV series "Scandal."
  • 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_69d81c5ba13c8190839315f54768acfd completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de02d8fb788190baef7537be2baecb completed April 14, 2026, 9:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c0f35ba48190b8b071679251233f completed May 3, 2026, 9:41 p.m.
Created at: April 9, 2026, 10:14 p.m.