Triple

T6975649
Position Surface form Disambiguated ID Type / Status
Subject Arrival E161709 entity
Predicate producer P490 FINISHED
Object Shawn Levy E68999 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: Shawn Levy | Statement: [Arrival, producer, Shawn Levy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shawn Levy
Context triple: [Arrival, producer, Shawn Levy]
  • A. Shawn Levy chosen
    Shawn Levy is a Canadian film director, producer, and actor best known for helming mainstream comedies and adventure films such as the "Night at the Museum" series and for producing hit television shows like "Stranger Things."
  • B. Peyton Reed
    Peyton Reed is an American film director known for helming major studio comedies and Marvel superhero films, including entries in the Ant-Man series.
  • C. Marc Turtletaub
    Marc Turtletaub is an American film producer and director known for backing acclaimed independent and character-driven movies such as "Little Miss Sunshine" and "A Beautiful Day in the Neighborhood."
  • D. Jordan Vogt-Roberts
    Jordan Vogt-Roberts is an American film director best known for helming the 2017 blockbuster monster movie "Kong: Skull Island."
  • E. Peter Segal
    Peter Segal is an American film director known for mainstream comedies such as "Tommy Boy," "50 First Dates," and "Get Smart."
  • 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_69c68854a0d88190bc0bf82263f1afce completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db3d3ab08190b107f3229c357dd2 completed March 27, 2026, 7:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69c761ab41b0819084d26c10bb763f8e completed March 28, 2026, 5:05 a.m.
Created at: March 27, 2026, 2:31 p.m.