Triple

T23206213
Position Surface form Disambiguated ID Type / Status
Subject Biritiba Mirim E580462 entity
Predicate neighboringMunicipality P17964 FINISHED
Object Mogi das Cruzes 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: Mogi das Cruzes | Statement: [Biritiba Mirim, neighboringMunicipality, Mogi das Cruzes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mogi das Cruzes
Context triple: [Biritiba Mirim, neighboringMunicipality, Mogi das Cruzes]
  • A. Mogi das Cruzes chosen
    Mogi das Cruzes is a municipality in southeastern Brazil known as part of the Greater São Paulo metropolitan area and recognized for its industrial activity and agricultural production.
  • B. São Caetano do Sul
    São Caetano do Sul is a highly urbanized and affluent city in the São Paulo metropolitan region of Brazil, known for its high quality of life and strong industrial and service sectors.
  • C. São Bernardo do Campo
    São Bernardo do Campo is a major industrial city in Brazil known as a key center of the automotive industry within the São Paulo metropolitan area.
  • D. Guarulhos
    Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
  • 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_69e24602ae1481908aaa6bc7ca493867 completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f1907cd62c8190afee1e963b170727 completed April 29, 2026, 5 a.m.
Created at: April 17, 2026, 4:07 p.m.