Triple

T9597773
Position Surface form Disambiguated ID Type / Status
Subject Paraíba do Sul River E231774 entity
Predicate passesThrough P225 FINISHED
Object São José dos Campos E760809 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: São José dos Campos | Statement: [Paraíba do Sul River, passesThrough, São José dos Campos]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: São José dos Campos
Context triple: [Paraíba do Sul River, passesThrough, São José dos Campos]
  • A. São José dos Campos chosen
    São José dos Campos is a major Brazilian city in the state of São Paulo known as an important hub for aerospace, technology, and industrial development.
  • B. Campinas
    Campinas is a major city in the state of São Paulo, Brazil, known as an important industrial, technological, and transportation hub in the country.
  • C. Guarulhos
    Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
  • D. São Carlos
    São Carlos is a Brazilian city in the state of São Paulo known as a major university and technology hub, hosting important campuses and research centers.
  • E. Campo Limpo Paulista
    Campo Limpo Paulista is a municipality in the metropolitan region of São Paulo, Brazil, known for its residential character and proximity to major industrial and urban centers.
  • 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_69ca8484838c8190b2049199d22fef70 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9a366d3481908db62e476958eafe completed April 1, 2026, 10:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1619f4170819092ae90b2896b0855 completed April 4, 2026, 7:08 p.m.
Created at: March 30, 2026, 8:07 p.m.