Triple

T10083831
Position Surface form Disambiguated ID Type / Status
Subject Peter Safran E213968 entity
Predicate employer P7 FINISHED
Object DC Studios E118509 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: DC Studios | Statement: [Peter Safran, employer, DC Studios]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DC Studios
Context triple: [Peter Safran, employer, DC Studios]
  • A. DC Studios chosen
    DC Studios is the film and television production division responsible for developing and overseeing live-action and animated projects based on DC Comics properties.
  • B. DC Films
    DC Films is a film production banner of Warner Bros. focused on creating movies based on DC Comics characters and properties.
  • C. DC Entertainment
    DC Entertainment is a media company and subsidiary of Warner Bros. responsible for managing and developing film, television, and other adaptations of DC Comics properties.
  • D. Marvel Studios
    Marvel Studios is a major American film and television production company best known for creating the Marvel Cinematic Universe of interconnected superhero movies and series.
  • E. DC Extended Universe
    The DC Extended Universe is a shared cinematic universe of superhero films and related media based on DC Comics characters, featuring interconnected stories and recurring characters across multiple movies.
  • 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_69ca839bf730819086900c323c9b8c95 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd04352d081908f676444cd2d2578 completed April 2, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b675f4b08190bd8285f210191b93 completed April 5, 2026, 7:22 p.m.
Created at: March 30, 2026, 9 p.m.