Triple

T22454463
Position Surface form Disambiguated ID Type / Status
Subject Mallrats E555078 entity
Predicate producer P490 FINISHED
Object Sean Daniel 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: Sean Daniel | Statement: [Mallrats, producer, Sean Daniel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sean Daniel
Context triple: [Mallrats, producer, Sean Daniel]
  • A. Sean Daniel chosen
    Sean Daniel is an American film producer known for working on major Hollywood genre films, including horror and fantasy franchises.
  • B. Ben Daniels
    Ben Daniels is an English actor known for his work in film, television, and theatre, including roles in projects such as the 2005 film adaptation of "Doom" and the series "The Exorcist" and "House of Cards."
  • C. Sean Sagar
    Sean Sagar is a British actor known for roles in television dramas and action series, including a part in the NCIS franchise spin-off NCIS: Sydney.
  • D. Paul Daniel
    Paul Daniel is a British conductor known for his leadership roles with major orchestras and opera companies, including English National Opera and the West Australian Symphony Orchestra.
  • E. Matt Weinberg
    Matt Weinberg is an American former child actor best known for his voice and on-screen roles in film and television during the late 1990s and early 2000s.
  • 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_69e11e5113208190ab58c6b595f9d1d0 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15b4e2bd4819083e5bed44e9776c6 completed April 29, 2026, 1:13 a.m.
Created at: April 16, 2026, 8:48 p.m.