Triple

T11478856
Position Surface form Disambiguated ID Type / Status
Subject São Carlos E272089 entity
Predicate locatedNear P294 FINISHED
Object Ribeirão Preto E239657 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: Ribeirão Preto | Statement: [São Carlos, locatedNear, Ribeirão Preto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ribeirão Preto
Context triple: [São Carlos, locatedNear, Ribeirão Preto]
  • A. Ribeirão Preto chosen
    Ribeirão Preto is a major city in the state of São Paulo, Brazil, known as an important economic and cultural center with a strong agribusiness and services sector.
  • B. São José do Rio Preto
    São José do Rio Preto is a major inland city in northwestern São Paulo state, Brazil, known as a regional economic, medical, and educational hub.
  • C. 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.
  • D. Bauru
    Bauru is a city in the state of São Paulo, Brazil, known as a regional economic and educational hub that hosts a campus of the University of São Paulo.
  • E. 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.
  • 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_69d6aae0c8d881908a5a360c0be3242e completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8294f0e948190b2e106beb86e4b2c completed April 9, 2026, 10:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69e68508d3ac8190888982eca2472919 completed April 20, 2026, 7:56 p.m.
Created at: April 8, 2026, 9:36 p.m.