Triple

T2461701
Position Surface form Disambiguated ID Type / Status
Subject Fast & Furious E54547 entity
Predicate stars P1956 FINISHED
Object Jordana Brewster E259266 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: Jordana Brewster | Statement: [Fast & Furious, stars, Jordana Brewster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jordana Brewster
Context triple: [Fast & Furious, stars, Jordana Brewster]
  • A. Jordana Brewster chosen
    Jordana Brewster is a Panamanian-American actress best known for her role as Mia Toretto in the Fast & Furious film franchise.
  • B. Sanaa Lathan
    Sanaa Lathan is an American actress known for her work in film, television, and voice acting, including prominent roles in movies like "Love & Basketball" and "Brown Sugar."
  • C. Michelle Rodriguez
    Michelle Rodriguez is an American actress best known for her tough, action-oriented roles, particularly as Letty Ortiz in the Fast & Furious film franchise.
  • D. Gabrielle Union
    Gabrielle Union is an American actress, author, and producer known for her roles in films like "Bring It On" and "Bad Boys II" as well as the TV series "Being Mary Jane."
  • E. Jessica Biel
    Jessica Biel is an American actress and producer known for her roles in the TV series "7th Heaven" and films such as "The Texas Chainsaw Massacre" and "The Illusionist."
  • 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_69ab49dee84c819096b50a0049c347ac completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd11c47408190b10c7f6a151f2db2 completed March 7, 2026, 7:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69af179665d081909f9a761fa50c44e7 completed March 9, 2026, 6:55 p.m.
Created at: March 6, 2026, 9:44 p.m.