Triple

T15600809
Position Surface form Disambiguated ID Type / Status
Subject Doctor Detroit E375026 entity
Predicate producer P490 FINISHED
Object Robert K. Weiss 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: Robert K. Weiss | Statement: [Doctor Detroit, producer, Robert K. Weiss]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Robert K. Weiss
Context triple: [Doctor Detroit, producer, Robert K. Weiss]
  • A. Robert K. Weiss chosen
    Robert K. Weiss is an American film and television producer best known for his work on comedy projects such as "The Naked Gun" series and collaborations with the Zucker brothers.
  • B. Daniel H. Weiss
    Daniel H. Weiss is an American art historian and academic leader who served as president and CEO of New York’s Metropolitan Museum of Art.
  • C. David C. Weiss
    David C. Weiss is an American attorney who serves as a U.S. Special Counsel and has been a key federal prosecutor in high-profile political and financial investigations.
  • D. John Weiss
    John Weiss is a relatively obscure individual whose specific notability is not clearly established from the given information.
  • E. Peter J. Weinberger
    Peter J. Weinberger is an American computer scientist known for his contributions to programming languages and tools at Bell Labs, including co-creating the AWK programming language.
  • 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_69d85cce25008190b13b52745fbd719b completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e621fc4819097e8e85e7ddfdc6c completed April 16, 2026, 2:50 a.m.
Created at: April 10, 2026, 4:12 a.m.