Triple

T15571622
Position Surface form Disambiguated ID Type / Status
Subject Campo Limpo Paulista E374255 entity
Predicate neighboringCity P988 FINISHED
Object Várzea Paulista E303893 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: Várzea Paulista | Statement: [Campo Limpo Paulista, neighboringCity, Várzea Paulista]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Várzea Paulista
Context triple: [Campo Limpo Paulista, neighboringCity, Várzea Paulista]
  • A. Várzea Paulista chosen
    Várzea Paulista is a municipality in southeastern Brazil known for its integration into the industrial and services corridor of the São Paulo metropolitan region.
  • B. Sapucaia
    Sapucaia is a municipality located in the mountainous Região Serrana of the state of Rio de Janeiro, Brazil.
  • C. Itapeva
    Itapeva is a municipality in the state of São Paulo, Brazil, known for its regional agricultural economy and educational institutions.
  • D. Paraopeba River
    The Paraopeba River is a significant waterway in southeastern Brazil that flows through the state of Minas Gerais and contributes substantially to the São Francisco River basin.
  • E. Vargem Grande Paulista
    Vargem Grande Paulista is a municipality in the state of São Paulo, Brazil, known for its semi-rural character and proximity to the São Paulo metropolitan area.
  • 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_69d85ccd575081908909b71a3f3e3a61 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e2025888190a2b6240296bba13e completed April 16, 2026, 2:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffa124e7d48190ac25e9541dea0122 completed May 9, 2026, 9:03 p.m.
Created at: April 10, 2026, 4:10 a.m.