Triple

T11942380
Position Surface form Disambiguated ID Type / Status
Subject Terminal 3 (São Paulo–Guarulhos International Airport) E284207 entity
Predicate locatedIn P40 FINISHED
Object 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: São Paulo | Statement: [Terminal 3 (São Paulo–Guarulhos International Airport), locatedIn, São Paulo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: São Paulo
Context triple: [Terminal 3 (São Paulo–Guarulhos International Airport), locatedIn, São Paulo]
  • A. São Paulo chosen
    São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
  • B. 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.
  • C. 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.
  • D. Guarulhos
    Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
  • E. Río de Janeiro
    Río de Janeiro is a station on Buenos Aires Underground Line A in Argentina’s capital city.
  • 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_69f49cd0f8e0819081e4492e0002e48a completed May 1, 2026, 12:30 p.m.
Created at: April 8, 2026, 9:45 p.m.