Triple

T5271512
Position Surface form Disambiguated ID Type / Status
Subject Emile Hirsch E119268 entity
Predicate playedCharacter P1507 FINISHED
Object Speed Racer in Speed Racer E85513 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: Speed Racer in Speed Racer | Statement: [Emile Hirsch, playedCharacter, Speed Racer in Speed Racer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Speed Racer in Speed Racer
Context triple: [Emile Hirsch, playedCharacter, Speed Racer in Speed Racer]
  • A. Speed Racer chosen
    Speed Racer is a 2008 live-action film adaptation of the classic Japanese anime and manga series, known for its hyper-stylized visuals and high-octane racing sequences.
  • B. Speed Racer (manga)
    Speed Racer (manga) is a classic 1960s Japanese racing manga series by Tatsuo Yoshida that follows the high-speed adventures of a young race car driver and his advanced car, the Mach 5.
  • C. Racers
    The Racers are the athletic teams representing Murray State University in intercollegiate sports.
  • D. Mom Racer
    Mom Racer is the caring and supportive mother of the protagonist in the 2008 live-action film adaptation of the classic anime Speed Racer.
  • E. Racer
    Racer is a classic wooden racing roller coaster located at Kennywood amusement park in Pennsylvania.
  • 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_69bd446c38e081908cdaf113bdf86790 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7c1fa01081909d589686289b624b completed March 20, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf10d4cd44819085193f0f76eb597a completed March 21, 2026, 9:42 p.m.
Created at: March 20, 2026, 1:51 p.m.