Triple

T21940574
Position Surface form Disambiguated ID Type / Status
Subject Ponta Negra neighborhood E541807 entity
Predicate municipality P852 FINISHED
Object Natal 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: Natal | Statement: [Ponta Negra neighborhood, municipality, Natal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Natal
Context triple: [Ponta Negra neighborhood, municipality, Natal]
  • A. Natal
    Natal is a historical region in southeastern South Africa, centered on the port city of Durban and known for its colonial history and diverse cultural heritage.
  • B. Natal chosen
    Natal is a coastal city in northeastern Brazil known for its beaches, sand dunes, and role as a regional tourism and economic hub.
  • C. Neive
    Neive is a picturesque medieval village in Italy’s Piedmont wine region, renowned for its historic charm and production of Barbaresco and other Langhe wines.
  • D. Nannini
    Nannini is an Italian surname most prominently associated with rock singer-songwriter Gianna Nannini and her family.
  • E. Bonita
    Bonita is a suburban community in southern San Diego County, California, known for its residential neighborhoods, golf courses, and proximity to the Sweetwater River.
  • 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_69e0c47e2e5c81909a7f74ce3de50911 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f12420b1cc81909b375891aedc0979 completed April 28, 2026, 9:18 p.m.
Created at: April 16, 2026, 7:55 p.m.