Triple

T13036079
Position Surface form Disambiguated ID Type / Status
Subject 42 (school) E326563 entity
Predicate hasCampus P116 FINISHED
Object 42 São Paulo E9033 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: 42 São Paulo | Statement: [42 (school), hasCampus, 42 São Paulo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 42 São Paulo
Context triple: [42 (school), hasCampus, 42 São Paulo]
  • A. Sé, São Paulo
    Sé, São Paulo is a historic central district of São Paulo, Brazil, known as the city's symbolic heart and home to major landmarks, including the main cathedral and the official city center marker.
  • B. São Paulo chosen
    São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
  • C. Guarulhos
    Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
  • D. Belo Horizonte
    Belo Horizonte is the capital and largest city of the Brazilian state of Minas Gerais, known for its modernist architecture, surrounding mountains, and vibrant cultural and economic life.
  • E. Bela Vista, São Paulo
    Bela Vista is a central neighborhood in São Paulo, Brazil, known for its Italian heritage, vibrant cultural life, and important landmarks such as major theaters and museums.
  • 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_69d8076cc45c81908123123f43e69266 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97f2a71a0819098bb6cf8a4b2208a completed April 10, 2026, 10:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7b8c0b24c819082d0ec947b7d99ea completed May 3, 2026, 9:06 p.m.
Created at: April 9, 2026, 8:55 p.m.