Triple

T3455987
Position Surface form Disambiguated ID Type / Status
Subject Reynolds E72904 entity
Predicate successorTeam P3901 FINISHED
Object Banesto E72905 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: Banesto | Statement: [Reynolds, successorTeam, Banesto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Banesto
Context triple: [Reynolds, successorTeam, Banesto]
  • A. Banesto chosen
    Banesto was a prominent Spanish professional cycling team, sponsored by the Banco Español de Crédito, best known for supporting Miguel Indurain during his multiple Tour de France victories in the early 1990s.
  • B. Bank of Spain
    The Bank of Spain is Spain’s central bank, responsible for national monetary policy and financial supervision, and is famously depicted as the main heist target in the TV series "Money Heist."
  • C. Banco Bilbao Vizcaya Argentaria
    Banco Bilbao Vizcaya Argentaria (BBVA) is a major multinational Spanish banking group that provides a wide range of financial services across Europe, the Americas, and other global markets.
  • D. Santander
    Santander is a historic port city in northern Spain, known for its maritime heritage, beaches, and role as the capital of the Cantabria region.
  • E. Banamex
    Banamex is one of Mexico’s largest and oldest banking institutions, offering a wide range of financial services to retail and corporate customers.
  • 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_69ad85b12a908190a1d10a6b03b4f8ae completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adbaa77b9c81909376a5995cdaf6ac completed March 8, 2026, 6:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69b367ff05a08190a0c4df5ebfb9741d completed March 13, 2026, 1:27 a.m.
Created at: March 8, 2026, 3:16 p.m.