Triple

T18822464
Position Surface form Disambiguated ID Type / Status
Subject Paraná E460294 entity
Predicate hasCity P316 FINISHED
Object São José dos Pinhais NE NERFINISHED

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 José dos Pinhais | Statement: [Paraná, hasCity, São José dos Pinhais]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: São José dos Pinhais
Context triple: [Paraná, hasCity, São José dos Pinhais]
  • A. São José dos Pinhais chosen
    São José dos Pinhais is an industrial and logistics hub in the Curitiba metropolitan area of Paraná, Brazil, known for its automotive plants and proximity to the region’s main international airport.
  • B. Guarapuava
    Guarapuava is a city in the state of Paraná, Brazil, known for its significant population of German Brazilians and its role as an agricultural and regional economic center.
  • C. Curití
    Curití is a small Colombian town in the Santander Department, known for its colonial architecture, natural landscapes, and traditional crafts.
  • D. Maringá
    Maringá is a planned, mid-20th-century city in the state of Paraná known for its green urban design, strong agricultural-based economy, and high quality of life.
  • E. Osasco
    Osasco is a major industrial and commercial city in the metropolitan region of São Paulo, Brazil.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dcf94c288190a06dea029ae4b223 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5a6bbc7148190819252071a765975 completed April 20, 2026, 4:08 a.m.
Created at: April 10, 2026, 11:55 a.m.