Triple

T1181024
Position Surface form Disambiguated ID Type / Status
Subject Money Monster E25136 entity
Predicate producer P490 FINISHED
Object Grant Heslov E138298 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: Grant Heslov | Statement: [Money Monster, producer, Grant Heslov]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Grant Heslov
Context triple: [Money Monster, producer, Grant Heslov]
  • A. Grant Heslov chosen
    Grant Heslov is an American actor, screenwriter, director, and producer best known for his frequent collaborations with George Clooney on acclaimed films such as "Good Night, and Good Luck" and "Argo."
  • B. David Seidler
    David Seidler is a British-American screenwriter best known for writing the Academy Award-winning screenplay for the historical drama film "The King’s Speech."
  • C. Justin Hollander
    Justin Hollander is a Major League Baseball executive who serves as the general manager of the Seattle Mariners, overseeing the club’s baseball operations and roster construction.
  • D. Michael Filerman
    Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
  • E. Charles Roven
    Charles Roven is an American film producer known for his work on major Hollywood blockbusters, including Christopher Nolan’s films such as Oppenheimer and The Dark Knight trilogy.
  • 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_69a494267b4c819088c97a59182bf56a completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bd32c5f48190b4e2d39fa052cbb7 completed March 1, 2026, 10:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbf15b1e08190a9c75bc7bc467197 completed March 8, 2026, 12:13 a.m.
Created at: March 1, 2026, 7:45 p.m.