Triple

T8655908
Position Surface form Disambiguated ID Type / Status
Subject Paraná (state) E205414 entity
Predicate hasCity P316 FINISHED
Object Ponta Grossa E651372 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: Ponta Grossa | Statement: [Paraná (state), hasCity, Ponta Grossa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ponta Grossa
Context triple: [Paraná (state), hasCity, Ponta Grossa]
  • A. Ponta Grossa chosen
    Ponta Grossa is a major industrial and commercial city in the state of Paraná, known as an important regional hub in southern Brazil.
  • B. Ribeira Grande
    Ribeira Grande is a coastal municipality and city on São Miguel Island in Portugal’s Azores archipelago, known for its historic center, hot springs, and dramatic volcanic landscapes.
  • C. Ribeira Grande
    Ribeira Grande is a coastal town and municipality on the island of Santo Antão in Cape Verde, known for its dramatic mountainous landscapes and traditional Cape Verdean culture.
  • 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. Brejo Grande
    Brejo Grande is a small coastal municipality in the Brazilian state of Sergipe, known for its location at the mouth of the São Francisco River.
  • 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_69ca8350897c819086cde7596fbe5fe7 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc4844586081909b687e278496eefa completed March 31, 2026, 10:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69cef373a22c8190931b4107c68e7017 completed April 2, 2026, 10:53 p.m.
Created at: March 30, 2026, 6:29 p.m.