Triple

T3926238
Position Surface form Disambiguated ID Type / Status
Subject To Catch a Thief E93283 entity
Predicate screenwriter P2831 FINISHED
Object John Michael Hayes E302114 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: John Michael Hayes | Statement: [To Catch a Thief, screenwriter, John Michael Hayes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Michael Hayes
Context triple: [To Catch a Thief, screenwriter, John Michael Hayes]
  • A. John Michael Hayes chosen
    John Michael Hayes was an American screenwriter best known for his collaborations with Alfred Hitchcock, including writing the screenplay for "Rear Window."
  • B. Peter Hayes
    Peter Hayes is a British diplomat and civil servant who has served in senior roles including Commissioner of the British Antarctic Territory.
  • C. Gregory J. Hayes
    Gregory J. Hayes is an American business executive best known for leading major aerospace and defense companies, including serving as CEO of RTX (formerly Raytheon Technologies).
  • D. Patrick Joseph Hayes
    Patrick Joseph Hayes was an American cardinal of the Roman Catholic Church who served as Archbishop of New York in the early 20th century.
  • E. John Requa
    John Requa is an American screenwriter and director known for co-writing films such as "Bad Santa," "I Love You Phillip Morris," and "Jungle Cruise."
  • 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_69aed96bfa1081908f7b30f2c647dee6 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeed80a1e48190aa39748b9db42701 completed March 9, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a82c551c8190a97fdc5a96cf131c completed March 14, 2026, 6:25 p.m.
Created at: March 9, 2026, 3:23 p.m.