Triple

T20724801
Position Surface form Disambiguated ID Type / Status
Subject Nordeste Linhas Aéreas Regionais E509405 entity
Predicate servedCity P3936 FINISHED
Object Fortaleza NE NERFINISHED

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: Fortaleza | Statement: [Nordeste Linhas Aéreas Regionais, servedCity, Fortaleza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fortaleza
Context triple: [Nordeste Linhas Aéreas Regionais, servedCity, Fortaleza]
  • A. Fortaleza chosen
    Fortaleza is a large coastal city in northeastern Brazil known for its beaches, tourism, and role as the capital of the state of Ceará.
  • B. Saquarema
    Saquarema is a coastal city in the state of Rio de Janeiro, Brazil, known for its beaches and strong surfing culture.
  • C. São Sebastião
    São Sebastião is a coastal municipality in the state of São Paulo, Brazil, known for its beaches, tourism, and role as a port city.
  • D. São Sebastião
    São Sebastião is a civil parish in the municipality of Ponta Delgada on São Miguel Island in Portugal’s Azores archipelago.
  • E. Jaboatão dos Guararapes
    Jaboatão dos Guararapes is a major coastal city in northeastern Brazil known for its historical significance in the Dutch-Portuguese conflicts and its integration into the metropolitan area of Recife.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4c4cc648190b45fda6e2b20af56 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c1e662f08190917ee043612d413e completed April 21, 2026, 12:16 a.m.
Created at: April 16, 2026, 12:28 p.m.