Triple

T16294770
Position Surface form Disambiguated ID Type / Status
Subject Getafe CF E395618 entity
Predicate hasRival P1375 FINISHED
Object Rayo Vallecano E143929 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: Rayo Vallecano | Statement: [Getafe CF, hasRival, Rayo Vallecano]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rayo Vallecano
Context triple: [Getafe CF, hasRival, Rayo Vallecano]
  • A. Rayo Vallecano chosen
    Rayo Vallecano is a Spanish football club from the Madrid neighborhood of Vallecas, known for its working-class identity and passionate local support.
  • B. Club of Madrid
    The Club of Madrid is an independent organization composed mainly of former heads of state and government that works to promote democracy and good governance worldwide.
  • C. Atlético Español
    Atlético Español was a Mexican professional football club based in Mexico City that competed in the country’s top division during the 1970s.
  • D. CF Rayo Majadahonda
    CF Rayo Majadahonda is a Spanish football club based in the Madrid suburb of Majadahonda, known for competing in the lower professional tiers of Spanish football.
  • E. Getafe
    Getafe is a city in central Spain that forms part of the Madrid metropolitan area and is known for its industrial base, university campus, and air force history.
  • 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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e25e2c255881909d99c43770475329 completed April 17, 2026, 4:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a001f9965b8819080278ccef15288aa completed May 10, 2026, 6:03 a.m.
Created at: April 10, 2026, 5:05 a.m.