Triple

T21116778
Position Surface form Disambiguated ID Type / Status
Subject Yes Man (film) E520319 entity
Predicate producer P490 FINISHED
Object Andrew Lazar NE NERFINISHED

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: Andrew Lazar | Statement: [Yes Man (film), producer, Andrew Lazar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Andrew Lazar
Context triple: [Yes Man (film), producer, Andrew Lazar]
  • A. Andrew Lazar chosen
    Andrew Lazar is an American film producer known for his work on movies such as "Get Smart" and other major studio comedies and dramas.
  • B. Edward Zorinsky
    Edward Zorinsky was a U.S. Senator from Nebraska and former mayor of Omaha known for his moderate Democratic politics and service in the late 20th century.
  • C. George Kozmetsky
    George Kozmetsky was an American technology entrepreneur, investor, and educator best known as a co-founder of Teledyne and a major figure in fostering innovation and high-tech industry growth.
  • D. Mark Antokolsky
    Mark Antokolsky was a renowned 19th-century Russian-Jewish sculptor celebrated for his realistic and emotionally expressive historical and religious works.
  • E. Edward Mezvinsky
    Edward Mezvinsky is an American former politician and lawyer best known for serving as a U.S. Congressman from Iowa and later being convicted in a high-profile fraud case.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b509a318819092fbbcb21d1fe603 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e72106a3b48190a0efa51a74ae21f0 completed April 21, 2026, 7:02 a.m.
Created at: April 16, 2026, 2:55 p.m.