Triple

T3239262
Position Surface form Disambiguated ID Type / Status
Subject The Cosby Show E67928 entity
Predicate creator P184 FINISHED
Object Ed. Weinberger E317634 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: Ed. Weinberger | Statement: [The Cosby Show, creator, Ed. Weinberger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ed. Weinberger
Context triple: [The Cosby Show, creator, Ed. Weinberger]
  • A. Ed. Weinberger chosen
    Ed. Weinberger is an American television producer and writer best known for his work on influential sitcoms such as The Mary Tyler Moore Show, Taxi, and The Cosby Show.
  • B. Richard Haldeman
    Richard Haldeman was an American journalist and public relations executive, best known as the father of H. R. Haldeman, President Richard Nixon’s White House Chief of Staff.
  • C. Herb Abramson
    Herb Abramson was an American record producer and music industry executive best known as a co-founder of the influential label Atlantic Records.
  • D. Paul Nitze
    Paul Nitze was a prominent American diplomat and defense strategist who played a key role in shaping U.S. Cold War military and nuclear policy.
  • E. Roy Vagelos
    Roy Vagelos is an American physician, scientist, and former CEO of Merck & Co., renowned for his leadership in the pharmaceutical industry and major philanthropic support of medical education.
  • 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_69ad858d27348190abb61c280b4c86a9 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaef4c0bc819095e4f84296fe7cb6 completed March 8, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2774f93448190b8493b457636ae48 completed March 12, 2026, 8:20 a.m.
Created at: March 8, 2026, 3:08 p.m.