Triple

T2720361
Position Surface form Disambiguated ID Type / Status
Subject State of São Paulo E60066 entity
Predicate hasCity P316 FINISHED
Object Taubaté
Taubaté is a historic industrial and educational city in southeastern Brazil, located in the Paraíba Valley between São Paulo and Rio de Janeiro.
 E322525 NE FINISHED

How this triple was built (4 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: Taubaté | Statement: [State of São Paulo, hasCity, Taubaté]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Taubaté
Context triple: [State of São Paulo, hasCity, Taubaté]
  • A. Guarulhos
    Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
  • B. 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.
  • C. Santo André
    Santo André is a major industrial and residential city in the São Paulo metropolitan region of Brazil.
  • D. Ribeirão Preto
    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.
  • E. Butantã, São Paulo
    Butantã is a district in western São Paulo best known for hosting the main campus of the University of São Paulo and several major research and cultural institutions.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Taubaté
Triple: [State of São Paulo, hasCity, Taubaté]
Generated description
Taubaté is a historic industrial and educational city in southeastern Brazil, located in the Paraíba Valley between São Paulo and Rio de Janeiro.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Taubaté
Target entity description: Taubaté is a historic industrial and educational city in southeastern Brazil, located in the Paraíba Valley between São Paulo and Rio de Janeiro.
  • A. Guarulhos
    Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
  • B. 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.
  • C. Santo André
    Santo André is a major industrial and residential city in the São Paulo metropolitan region of Brazil.
  • D. Ribeirão Preto
    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.
  • E. Butantã, São Paulo
    Butantã is a district in western São Paulo best known for hosting the main campus of the University of São Paulo and several major research and cultural institutions.
  • F. None of above. chosen

Provenance (5 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_69ab4b746d248190958e052045c09255 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdab06d388190acf690787fe58ab5 completed March 7, 2026, 7:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69b1eecec6888190a0d8cb0729856bed completed March 11, 2026, 10:38 p.m.
NEDg Description generation batch_69b1ef5bbec4819082757bb3ddd614ff completed March 11, 2026, 10:40 p.m.
NED2 Entity disambiguation (via description) batch_69b1efdfba0081908e3e30faa8d0f862 completed March 11, 2026, 10:42 p.m.
Created at: March 6, 2026, 9:55 p.m.