Triple

T2720421
Position Surface form Disambiguated ID Type / Status
Subject State of São Paulo E60066 entity
Predicate hasCity P316 FINISHED
Object Atibaia
Atibaia is a municipality in southeastern Brazil known for its mild climate, flower and strawberry production, and proximity to São Paulo city.
E370647 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: Atibaia | Statement: [State of São Paulo, hasCity, Atibaia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Atibaia
Context triple: [State of São Paulo, hasCity, Atibaia]
  • A. Itanhaém
    Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
  • B. Taubaté
    Taubaté is a historic industrial and educational city in southeastern Brazil, located in the Paraíba Valley between São Paulo and Rio de Janeiro.
  • C. Barretos
    Barretos is a municipality in the Brazilian state of São Paulo, widely known for hosting one of the largest annual rodeo festivals in Latin America.
  • D. Itaquaquecetuba
    Itaquaquecetuba is a municipality in the Greater São Paulo metropolitan area of southeastern Brazil, known for its rapid urban growth and industrial activity.
  • E. Santo Amaro
    Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
  • 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: Atibaia
Triple: [State of São Paulo, hasCity, Atibaia]
Generated description
Atibaia is a municipality in southeastern Brazil known for its mild climate, flower and strawberry production, and proximity to São Paulo city.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Atibaia
Target entity description: Atibaia is a municipality in southeastern Brazil known for its mild climate, flower and strawberry production, and proximity to São Paulo city.
  • A. Itanhaém
    Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
  • B. Taubaté
    Taubaté is a historic industrial and educational city in southeastern Brazil, located in the Paraíba Valley between São Paulo and Rio de Janeiro.
  • C. Barretos
    Barretos is a municipality in the Brazilian state of São Paulo, widely known for hosting one of the largest annual rodeo festivals in Latin America.
  • D. Itaquaquecetuba
    Itaquaquecetuba is a municipality in the Greater São Paulo metropolitan area of southeastern Brazil, known for its rapid urban growth and industrial activity.
  • E. Santo Amaro
    Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
  • 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_69b402b005d88190b127d483ee48c2cd completed March 13, 2026, 12:27 p.m.
NEDg Description generation batch_69b4034973c08190b0332a9a8b5340d7 completed March 13, 2026, 12:30 p.m.
NED2 Entity disambiguation (via description) batch_69b4086ab034819086c5fa7d4b172d75 completed March 13, 2026, 12:51 p.m.
Created at: March 6, 2026, 9:55 p.m.