Triple

T1169946
Position Surface form Disambiguated ID Type / Status
Subject Recife E24891 entity
Predicate hasNickname P39 FINISHED
Object Venice of Brazil E132974 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: Venice of Brazil | Statement: [Recife, hasNickname, Venice of Brazil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Venice of Brazil
Context triple: [Recife, hasNickname, Venice of Brazil]
  • A. Brazilian Venice chosen
    Brazilian Venice is a popular nickname for Recife, a coastal Brazilian city known for its many rivers, bridges, and canals.
  • B. Salvador, Bahia, Brazil
    Salvador, the capital of Brazil’s Bahia state, is a major coastal city known for its Afro-Brazilian culture, colonial architecture, and historic role as the country’s first capital.
  • C. Rio de Janeiro
    Rio de Janeiro is a major Brazilian coastal city famed for its stunning beaches, dramatic landscape, Carnival festival, and iconic Christ the Redeemer statue.
  • D. Salvador
    Salvador is the given name of the renowned Spanish surrealist artist Salvador Dalí.
  • E. Chiado
    Chiado is a historic and upscale neighborhood in central Lisbon known for its elegant shops, cafés, theaters, and literary heritage.
  • 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_69a494082a7c819095004f423f294a64 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bce821b481908bc278a3fa7973f4 completed March 1, 2026, 10:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac6f17aa608190920b7df62b8dd903 completed March 7, 2026, 6:31 p.m.
Created at: March 1, 2026, 7:45 p.m.