Triple

T10063958
Position Surface form Disambiguated ID Type / Status
Subject Arena Pernambuco E213053 entity
Predicate locatedIn P40 FINISHED
Object São Lourenço da Mata E597271 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: São Lourenço da Mata | Statement: [Arena Pernambuco, locatedIn, São Lourenço da Mata]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: São Lourenço da Mata
Context triple: [Arena Pernambuco, locatedIn, São Lourenço da Mata]
  • A. São Lourenço da Mata chosen
    São Lourenço da Mata is a municipality in the Recife metropolitan region of Pernambuco, Brazil, known for its role in the 2014 FIFA World Cup infrastructure and its mix of urban and forested areas.
  • B. Pampilhosa da Serra
    Pampilhosa da Serra is a small municipality in central Portugal known for its mountainous landscapes, schist villages, and forested river valleys.
  • C. Laranjal Paulista
    Laranjal Paulista is a municipality in the state of São Paulo, Brazil, known for its riverside setting and regional agricultural activities.
  • D. Morrinhos
    Morrinhos is a municipality in the Brazilian state of Goiás, known for its agricultural economy and regional thermal springs.
  • E. Taboão da Serra
    Taboão da Serra is a densely populated municipality in the São Paulo metropolitan area in southeastern 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_69ca83977128819084084eb7d1d8c52a completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdcfd4e4ac8190a37061b4082caa48 completed April 2, 2026, 2:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b630ca008190a337660ad8c9d57e completed April 5, 2026, 7:21 p.m.
Created at: March 30, 2026, 8:58 p.m.