Triple

T12358943
Position Surface form Disambiguated ID Type / Status
Subject Itapira E294682 entity
Predicate hasNeighboringMunicipality P224 FINISHED
Object Mogi Guaçu E350286 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: Mogi Guaçu | Statement: [Itapira, hasNeighboringMunicipality, Mogi Guaçu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mogi Guaçu
Context triple: [Itapira, hasNeighboringMunicipality, Mogi Guaçu]
  • A. Mogi Guaçu chosen
    Mogi Guaçu is a municipality in the interior of Brazil’s São Paulo state, known for its industrial activity and the Mogi Guaçu River that runs through it.
  • B. Barueri
    Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
  • C. Mogi Mirim
    Mogi Mirim is a municipality in the state of São Paulo, Brazil, known for its agricultural economy and regional football club, Mogi Mirim Esporte Clube.
  • D. Jundiaí
    Jundiaí is a mid-sized industrial and logistics city in southeastern Brazil known for its strong economy and high quality of life.
  • 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_69d6ab6d8a4081908636601e69ddf262 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f90201481909359416b8b9f7871 completed April 10, 2026, 6:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7c6f1e29c8190b073c3293cf68cb2 completed May 3, 2026, 10:06 p.m.
Created at: April 8, 2026, 9:54 p.m.