Triple

T14235184
Position Surface form Disambiguated ID Type / Status
Subject Race to Witch Mountain E352858 entity
Predicate screenwriter P2831 FINISHED
Object Matt Lopez E669906 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: Matt Lopez | Statement: [Race to Witch Mountain, screenwriter, Matt Lopez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Matt Lopez
Context triple: [Race to Witch Mountain, screenwriter, Matt Lopez]
  • A. Matt Lopez chosen
    Matt Lopez is an American screenwriter known for writing family-oriented films such as "Bedtime Stories" and other Hollywood studio projects.
  • B. Matt Alvarez
    Matt Alvarez is a film producer known for his work on various feature films, including the comedy "First Sunday."
  • C. Daniel Lugo
    Daniel Lugo is the ambitious, bodybuilding ringleader of the criminal scheme at the center of the dark comedy crime film "Pain & Gain."
  • D. Brian Pimental
    Brian Pimental is an American animator, storyboard artist, and director known for his work on several major Disney animated films, including contributing to the story of Beauty and the Beast.
  • E. Matt Martinez
    Matt Martinez is an American former Miami Hurricanes football player and firefighter best known as the ex-husband of supermodel Niki Taylor.
  • 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_69d8278adc7c8190a9218d69bce3c4e6 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de62411c888190a154acd56fe3fcaf completed April 14, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd6d77e8dc8190b90f3505960e549e completed May 8, 2026, 4:58 a.m.
Created at: April 10, 2026, 1:07 a.m.