Triple

T17106754
Position Surface form Disambiguated ID Type / Status
Subject Baixada Santista E415119 entity
Predicate containsCity P294 FINISHED
Object Cubatão E359555 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: Cubatão | Statement: [Baixada Santista, containsCity, Cubatão]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cubatão
Context triple: [Baixada Santista, containsCity, Cubatão]
  • A. Cubatão chosen
    Cubatão is an industrial city in southeastern Brazil known for its major petrochemical and steel complexes and its location near the port of Santos in the state of São Paulo.
  • B. Itapetininga
    Itapetininga is a municipality in southeastern Brazil known for its agricultural activities and regional commercial importance within the state of São Paulo.
  • C. Duas Barras
    Duas Barras is a small municipality in the mountainous interior of Rio de Janeiro state in southeastern Brazil.
  • D. Itanhaém
    Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
  • E. Barueri
    Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
  • 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_69d886cfc8e88190b05ba466edd35591 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dc2750b481908de18e8cb8f2195c completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a014143edb081909509c5435d392dd0 completed May 11, 2026, 2:39 a.m.
Created at: April 10, 2026, 5:35 a.m.