Triple

T11942113
Position Surface form Disambiguated ID Type / Status
Subject São Paulo metropolitan area E284201 entity
Predicate hasMunicipality P847 FINISHED
Object Mogi das Cruzes E348276 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 das Cruzes | Statement: [São Paulo metropolitan area, hasMunicipality, Mogi das Cruzes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mogi das Cruzes
Context triple: [São Paulo metropolitan area, hasMunicipality, 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 (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_69d6ab2db38c8190b1f0ed6663ef8ada completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90342bb908190a019ac91a2b82f3d completed April 10, 2026, 2:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69f64b7e6f0c8190b9df17f969df0deb completed May 2, 2026, 7:07 p.m.
Created at: April 8, 2026, 9:45 p.m.