Triple

T5772152
Position Surface form Disambiguated ID Type / Status
Subject 2014 FIFA World Cup E127353 entity
Predicate hostCity P1798 FINISHED
Object Cuiabá E144203 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: Cuiabá | Statement: [2014 FIFA World Cup, hostCity, Cuiabá]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cuiabá
Context triple: [2014 FIFA World Cup, hostCity, Cuiabá]
  • A. Cuiabá chosen
    Cuiabá is the capital city of Brazil’s Mato Grosso state and a primary urban hub and access point for exploring the Pantanal wetlands.
  • B. Lajeado
    Lajeado is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
  • C. Londrina
    Londrina is a major city in the southern Brazilian state of Paraná known for its significant Japanese Brazilian community and strong agricultural-based economy.
  • D. Mourão
    Mourão is a small municipality in Portugal’s Alentejo region, known for its historic castle and proximity to the Alqueva Reservoir.
  • E. Magé
    Magé is a municipality in the state of Rio de Janeiro, Brazil, located in the metropolitan region of Rio de Janeiro and known for its coastal setting and historical significance.
  • 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_69c00834f6308190851b0abeddd8ed7e completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c029adda188190a5c26c363614145f completed March 22, 2026, 5:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07e686ad88190b34e5e94145b44dc completed March 22, 2026, 11:42 p.m.
Created at: March 22, 2026, 3:50 p.m.