Triple

T11942115
Position Surface form Disambiguated ID Type / Status
Subject São Paulo metropolitan area E284201 entity
Predicate hasMunicipality P847 FINISHED
Object Poá E293519 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: Poá | Statement: [São Paulo metropolitan area, hasMunicipality, Poá]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Poá
Context triple: [São Paulo metropolitan area, hasMunicipality, Poá]
  • A. Poá chosen
    Poá is a municipality in the eastern part of the São Paulo metropolitan region in Brazil, known for its residential character and proximity to the capital city.
  • B. Parea
    Parea is a small coastal village on the island of Huahine in French Polynesia, known for its tranquil beaches and traditional Polynesian atmosphere.
  • C. Paupisi
    Paupisi is a small Italian municipality located in the Campania region, known for its rural character and proximity to the city of Benevento.
  • D. Pökoot
    Pökoot is a Nilotic language spoken primarily by the Pokot people in Kenya and Uganda.
  • E. Pias
    Pias is a civil parish located in the municipality of Serpa in Portugal’s Alentejo region.
  • 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_69f440a5a9c8819086a94ad60c6881b8 completed May 1, 2026, 5:56 a.m.
Created at: April 8, 2026, 9:45 p.m.