Triple

T11942087
Position Surface form Disambiguated ID Type / Status
Subject São Paulo metropolitan area E284201 entity
Predicate alsoKnownAs P39 FINISHED
Object Grande São Paulo E284201 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: Grande São Paulo | Statement: [São Paulo metropolitan area, alsoKnownAs, Grande São Paulo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Grande São Paulo
Context triple: [São Paulo metropolitan area, alsoKnownAs, Grande São Paulo]
  • A. São Paulo metropolitan area chosen
    The São Paulo metropolitan area is Brazil’s largest and most populous urban agglomeration, encompassing the city of São Paulo and numerous surrounding municipalities that form a major economic and cultural hub in Latin America.
  • B. São Bernardo do Campo
    São Bernardo do Campo is a major industrial city in Brazil known as a key center of the automotive industry within the São Paulo metropolitan area.
  • C. São Paulo
    São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
  • D. Santo André
    Santo André is a major industrial and residential city in the São Paulo metropolitan region of Brazil.
  • E. Santo André
    Santo André is a civil parish in the municipality of Santiago do Cacém in Portugal, known for its coastal location and proximity to the Sines industrial and port complex.
  • 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_69d6ab2db38c8190b1f0ed6663ef8ada completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90342bb908190a019ac91a2b82f3d completed April 10, 2026, 2:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69f655515be48190a0793eef7b016852 completed May 2, 2026, 7:49 p.m.
Created at: April 8, 2026, 9:45 p.m.