Triple

T8554573
Position Surface form Disambiguated ID Type / Status
Subject CBF E202532 entity
Predicate represents P129 FINISHED
Object Brazil in FIFA E52616 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: Brazil in FIFA | Statement: [CBF, represents, Brazil in FIFA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brazil in FIFA
Context triple: [CBF, represents, Brazil in FIFA]
  • A. Brazil national football team chosen
    The Brazil national football team is one of the most successful and iconic teams in world football history, renowned for its attacking style and record number of FIFA World Cup titles.
  • B. Brazil vs Germany
    Brazil vs Germany refers to the infamous 2014 FIFA World Cup semifinal in which Germany defeated host nation Brazil 7–1, one of the most shocking and lopsided matches in World Cup history.
  • C. Brazil
    Brazil is the largest country in South America, known for its vast Amazon rainforest, diverse culture, and major cities like São Paulo and Rio de Janeiro.
  • D. Brazil
    Brazil is a 1985 dystopian science fiction film known for its darkly satirical portrayal of a bureaucratic, totalitarian society and its distinctive, surreal visual style.
  • E. CAF Brasil
    CAF Brasil is the Brazilian subsidiary of the Spanish rolling stock manufacturer CAF, focused on supplying and maintaining railway and urban transit vehicles in Brazil.
  • 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_69ca832610e08190b3b6c6cd2c250255 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe88a936c8190a0234bf7da2ff55a completed March 31, 2026, 3:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce6dd67d288190a147562a99ecde56 completed April 2, 2026, 1:23 p.m.
Created at: March 30, 2026, 6:19 p.m.