Triple

T3111470
Position Surface form Disambiguated ID Type / Status
Subject The Mary Tyler Moore Show E64960 entity
Predicate starring P1507 FINISHED
Object Ted Knight E226047 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: Ted Knight | Statement: [The Mary Tyler Moore Show, starring, Ted Knight]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ted Knight
Context triple: [The Mary Tyler Moore Show, starring, Ted Knight]
  • A. Ted Knight chosen
    Ted Knight was an American actor and comedian best known for his Emmy-winning role as the pompous newscaster Ted Baxter on the television sitcom "The Mary Tyler Moore Show."
  • B. Carroll O’Connor
    Carroll O’Connor was an American actor best known for his iconic portrayal of Archie Bunker on the groundbreaking television sitcom "All in the Family."
  • C. Bob Crane
    Bob Crane was an American actor and disc jockey best known for starring as Colonel Hogan in the 1960s television sitcom "Hogan's Heroes."
  • D. Peter D. Graves
    Peter D. Graves is a film producer best known for his work on major Hollywood action and science fiction movies, including Terminator Salvation.
  • E. Frankie Faison
    Frankie Faison is an American actor known for his character roles in film and television, including appearances in projects like "The Wire," the "Hannibal Lecter" film series, and numerous comedies and dramas.
  • 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_69ad857eeaf48190b34ebfdaa7a264cf completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada43954f0819096a96331bf3c53a8 completed March 8, 2026, 4:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2e817b0d881909365a6bf5eb5104b completed March 12, 2026, 4:21 p.m.
Created at: March 8, 2026, 3:04 p.m.