Triple

T10544359
Position Surface form Disambiguated ID Type / Status
Subject Dino Brewster E248776 entity
Predicate enemyOf P437 FINISHED
Object Tobey Marshall E241424 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: Tobey Marshall | Statement: [Dino Brewster, enemyOf, Tobey Marshall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tobey Marshall
Context triple: [Dino Brewster, enemyOf, Tobey Marshall]
  • A. Tobey Marshall chosen
    Tobey Marshall is a skilled street racer and mechanic who becomes the protagonist seeking revenge and redemption in the 2014 action film "Need for Speed."
  • B. Tobin Powell Heath
    Tobin Powell Heath is an American professional soccer player renowned as a creative winger and multiple-time FIFA Women's World Cup champion with the United States women's national team.
  • C. Timothy Clemons
    Timothy Clemons is an individual whose specific public background or notable achievements are not clearly identifiable from the available information.
  • D. Derrick Marks
    Derrick Marks is an American former college basketball guard best known for starring at Boise State University, where he emerged as one of the Mountain West Conference’s top scorers and leaders.
  • E. Dane Witherspoon
    Dane Witherspoon was an American actor best known for his roles in daytime soap operas during the 1980s.
  • 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_69d381c733c08190ab1dd6239f5f34ae completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d519128cac819086c93f3bab854ac2 completed April 7, 2026, 2:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69d9343c5c308190952596e5254b6a65 completed April 10, 2026, 5:32 p.m.
Created at: April 6, 2026, 12:32 p.m.