Triple

T5672206
Position Surface form Disambiguated ID Type / Status
Subject Pol Espargaró E125003 entity
Predicate hasCompetitor P1375 FINISHED
Object Marc Márquez E392686 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: Marc Márquez | Statement: [Pol Espargaró, hasCompetitor, Marc Márquez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marc Márquez
Context triple: [Pol Espargaró, hasCompetitor, Marc Márquez]
  • A. Marc Márquez chosen
    Marc Márquez is a Spanish Grand Prix motorcycle road racer widely regarded as one of the greatest MotoGP riders of all time, known for his multiple world championships and aggressive riding style.
  • B. Jorge Lorenzo
    Jorge Lorenzo is a Spanish Grand Prix motorcycle road racer and multiple-time MotoGP World Champion known for his smooth, precise riding style.
  • C. Pol Espargaró
    Pol Espargaró is a Spanish Grand Prix motorcycle road racer known for competing in the MotoGP World Championship with multiple factory and satellite teams.
  • D. Valentino Rossi
    Valentino Rossi is an Italian motorcycle road racer widely regarded as one of the greatest MotoGP riders of all time, known for his multiple world championships and charismatic personality.
  • E. Alex Marquez
    Alex Marquez is a film editor known for his work on projects such as the 2016 biographical thriller "Snowden."
  • 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_69c008295c808190acfe78915e7d656a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0236e8dc48190b4eb7709a258909f completed March 22, 2026, 5:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04db5c9a08190b1d6b7db87d0d9ac completed March 22, 2026, 8:14 p.m.
Created at: March 22, 2026, 3:43 p.m.