Triple

T18056375
Position Surface form Disambiguated ID Type / Status
Subject The Replacements E432048 entity
Predicate castMember P1668 FINISHED
Object David Denman 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: David Denman | Statement: [The Replacements, castMember, David Denman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: David Denman
Context triple: [The Replacements, castMember, David Denman]
  • A. David Denman chosen
    David Denman is an American actor best known for his role as Roy Anderson on the U.S. version of "The Office" and for supporting performances in films and television series across comedy and drama.
  • B. David Denny
    David Denny was a 19th-century American pioneer and early settler of Seattle, Washington, who played a key role in the city's founding and development.
  • C. Dave Dennison
    Dave Dennison is a fictional character associated with Dani Dennison in the Hocus Pocus film universe, depicted as a member of her family.
  • D. David Brisbin
    David Brisbin is an American character actor known for his supporting roles in film and television, including appearances in projects like "Fear and Loathing in Las Vegas" and "Twin Peaks."
  • E. Jeff Danna
    Jeff Danna is a Canadian film composer known for his scores for movies such as The Boondock Saints and various animated and dramatic films.
  • 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_69d8b906482481908183315b9ecf9994 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4c102d08081908d2419c898213400 completed April 19, 2026, 11:48 a.m.
Created at: April 10, 2026, 10:26 a.m.