Triple

T20060367
Position Surface form Disambiguated ID Type / Status
Subject Ludacris E499453 entity
Predicate portrayed P1668 FINISHED
Object Tej Parker NE NERFINISHED

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: Tej Parker | Statement: [Ludacris, portrayed, Tej Parker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tej Parker
Context triple: [Ludacris, portrayed, Tej Parker]
  • A. Tej Parker chosen
    Tej Parker is a tech-savvy mechanic and hacker in the Fast & Furious film franchise, known for his intelligence, humor, and close partnership with Roman Pearce.
  • B. Kim Parker
    Kim Parker is a comedic, outspoken teenage character from the sitcom "Moesha," later becoming a central figure in its spin-off series "The Parkers."
  • C. Joy Parker
    Joy Parker was the wife of acclaimed English actor Paul Scofield, with whom she shared a long marriage and family life.
  • D. Christie Parker
    Christie Parker is a fictional character played by actress Jennifer Crystal Foley, best known from her work in American television.
  • E. Carol Parker
    Carol Parker is best known as the wife of Marlon Jackson, a member of the famed Jackson family and former singer of The Jackson 5.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da6276bcf48190aabbf279192a5fb4 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66374f4a48190beb575a6c84ebdb4 completed April 20, 2026, 5:33 p.m.
Created at: April 11, 2026, 3:38 p.m.