Triple

T3858972
Position Surface form Disambiguated ID Type / Status
Subject Funny People E90087 entity
Predicate producer P490 FINISHED
Object Clayton Townsend E298757 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: Clayton Townsend | Statement: [Funny People, producer, Clayton Townsend]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Clayton Townsend
Context triple: [Funny People, producer, Clayton Townsend]
  • A. Clayton Townsend chosen
    Clayton Townsend is a film producer known for his work on major Hollywood comedies and dramas, including the hit movie "Bridesmaids."
  • B. Steven M. Tipton
    Steven M. Tipton is an American sociologist of religion and ethics known for his collaborative work on the role of religion and moral values in contemporary American life.
  • C. Greg Stillson
    Greg Stillson is the ambitious, populist politician and primary antagonist in Stephen King’s novel "The Dead Zone," whose rise to power is foreseen to lead to catastrophic consequences.
  • D. John Dandridge
    John Dandridge was a Virginia planter and colonial official best known as the father of Martha Washington, the first First Lady of the United States.
  • E. Thad Luckinbill
    Thad Luckinbill is an American actor and film producer known for roles on "The Young and the Restless" and for producing acclaimed films such as "Sicario."
  • 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_69aed95b3c088190a8f85d19e6070599 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec1ff39c8190b83a88abd840a0e3 completed March 9, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b53fe645608190a26c47a74818560f completed March 14, 2026, 11 a.m.
Created at: March 9, 2026, 3:19 p.m.