Triple

T23056506
Position Surface form Disambiguated ID Type / Status
Subject Robbie Brenner E574168 entity
Predicate employer P7 FINISHED
Object Mattel Films 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: Mattel Films | Statement: [Robbie Brenner, employer, Mattel Films]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mattel Films
Context triple: [Robbie Brenner, employer, Mattel Films]
  • A. Mattel Films chosen
    Mattel Films is the film production division of the toy company Mattel, responsible for developing and producing movies based on its toy and game brands.
  • B. Mattel
    Mattel is a major American toy manufacturing company best known for creating iconic brands such as Barbie, Hot Wheels, and Fisher-Price.
  • C. Mattel Creations
    Mattel Creations is a content and entertainment division of Mattel that develops and produces media and brand experiences for franchises such as Barbie.
  • D. Mattel Interactive
    Mattel Interactive was the video game publishing division of the toy company Mattel, known for releasing games based on its popular toy and entertainment franchises.
  • E. Paramount Animation
    Paramount Animation is the animation division of Paramount Pictures, responsible for producing and developing the studio’s animated feature 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_69e245ba7ae48190be606dbc54120e39 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1868099708190b23725dc8a305e09 completed April 29, 2026, 4:18 a.m.
Created at: April 17, 2026, 3:55 p.m.