Triple

T5584912
Position Surface form Disambiguated ID Type / Status
Subject Imogen Poots E146729 entity
Predicate portrayedIn P626 FINISHED
Object Need for Speed E50450 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: Need for Speed | Statement: [Imogen Poots, portrayedIn, Need for Speed]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Need for Speed
Context triple: [Imogen Poots, portrayedIn, Need for Speed]
  • A. Need for Speed (video game series)
    Need for Speed is a long-running racing video game franchise known for its high-speed street racing, car customization, and police chases.
  • B. Need for Speed (2014 film) chosen
    Need for Speed (2014 film) is an action-packed racing movie based on the popular video game series, following a street racer seeking revenge through high-stakes cross-country car battles.
  • C. Gran Turismo
    Gran Turismo is a long-running and highly realistic racing simulation video game series known for its extensive car roster and meticulous attention to driving physics.
  • D. Jet Set Radio
    Jet Set Radio is a stylish cel-shaded action game known for its graffiti-tagging, inline skating, and influential soundtrack set in a futuristic Tokyo.
  • 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_69c0090287a08190b4098411effe970c completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c02085d0e48190b8d185fe7f3d8579 completed March 22, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04d2aab348190944cca5375e0ddb9 completed March 22, 2026, 8:12 p.m.
Created at: March 22, 2026, 3:37 p.m.