Triple

T2720414
Position Surface form Disambiguated ID Type / Status
Subject State of São Paulo E60066 entity
Predicate hasCity P316 FINISHED
Object Cotia
Cotia is a municipality in the metropolitan region of São Paulo, Brazil, known for its residential areas, green spaces, and proximity to the capital city.
E329369 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: Cotia | Statement: [State of São Paulo, hasCity, Cotia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cotia
Context triple: [State of São Paulo, hasCity, Cotia]
  • A. Itaquaquecetuba
    Itaquaquecetuba is a municipality in the Greater São Paulo metropolitan area of southeastern Brazil, known for its rapid urban growth and industrial activity.
  • B. Parnamirim
    Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
  • C. Limeira
    Limeira is a municipality in the interior of the Brazilian state of São Paulo, known for its industrial activity and history in the jewelry and citrus sectors.
  • D. Jacareí
    Jacareí is a municipality in southeastern Brazil known as part of the industrial and technological corridor within the state of São Paulo.
  • 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: Cotia
Triple: [State of São Paulo, hasCity, Cotia]
Generated description
Cotia is a municipality in the metropolitan region of São Paulo, Brazil, known for its residential areas, green spaces, and proximity to the capital city.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Cotia
Target entity description: Cotia is a municipality in the metropolitan region of São Paulo, Brazil, known for its residential areas, green spaces, and proximity to the capital city.
  • A. Itaquaquecetuba
    Itaquaquecetuba is a municipality in the Greater São Paulo metropolitan area of southeastern Brazil, known for its rapid urban growth and industrial activity.
  • B. Parnamirim
    Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
  • C. Limeira
    Limeira is a municipality in the interior of the Brazilian state of São Paulo, known for its industrial activity and history in the jewelry and citrus sectors.
  • D. Jacareí
    Jacareí is a municipality in southeastern Brazil known as part of the industrial and technological corridor within the state of São Paulo.
  • 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_69b20f26de488190ac6050638278f988 completed March 12, 2026, 12:56 a.m.
NEDg Description generation batch_69b212d7ec248190a3f55f5efdd85f97 completed March 12, 2026, 1:11 a.m.
NED2 Entity disambiguation (via description) batch_69b2134ca9d881908676b9d349767e0d completed March 12, 2026, 1:13 a.m.
Created at: March 6, 2026, 9:55 p.m.