Triple

T7499383
Position Surface form Disambiguated ID Type / Status
Subject Die Hard with a Vengeance E177219 entity
Predicate producer P490 FINISHED
Object Michael Tadross E540789 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: Michael Tadross | Statement: [Die Hard with a Vengeance, producer, Michael Tadross]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Tadross
Context triple: [Die Hard with a Vengeance, producer, Michael Tadross]
  • A. Michael Tadross chosen
    Michael Tadross is an American film producer known for working on major Hollywood productions, including big-budget action and fantasy films.
  • B. Jeffrey Stott
    Jeffrey Stott is a film producer best known for his work on the political comedy film "The American President."
  • C. Paul Torday
    Paul Torday was a British novelist best known for his satirical debut novel "Salmon Fishing in the Yemen," which brought him widespread recognition later in life.
  • D. Jeffrey S. Mearns
    Jeffrey S. Mearns is an American academic leader and attorney who serves as the president of Ball State University in Indiana.
  • E. Andrew D. Martin
    Andrew D. Martin is an American political scientist and academic administrator who serves as the chancellor of Washington University in St. Louis.
  • 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_69c69f2696688190915a8458f2398211 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f598dfac8190a123daaac0784aee completed March 27, 2026, 9:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c900ad081908506a2097f7fd30b completed March 28, 2026, 8:39 p.m.
Created at: March 27, 2026, 3:44 p.m.