Triple

T13951299
Position Surface form Disambiguated ID Type / Status
Subject Guarujá E335529 entity
Predicate distanceFrom P1299 FINISHED
Object Sao Paulo city E9033 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: Sao Paulo city | Statement: [Guarujá, distanceFrom, Sao Paulo city]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sao Paulo city
Context triple: [Guarujá, distanceFrom, Sao Paulo city]
  • A. São Paulo chosen
    São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
  • B. Sé, São Paulo
    Sé, São Paulo is a historic central district of São Paulo, Brazil, known as the city's symbolic heart and home to major landmarks, including the main cathedral and the official city center marker.
  • C. Belo Horizonte
    Belo Horizonte is the capital and largest city of the Brazilian state of Minas Gerais, known for its modernist architecture, surrounding mountains, and vibrant cultural and economic life.
  • D. Río de Janeiro
    Río de Janeiro is a station on Buenos Aires Underground Line A in Argentina’s capital city.
  • E. 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.
  • 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_69d81c6081b88190b53e317c3370c8fe completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2e131c608190b4ffdbada24a3208 completed April 14, 2026, 12:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcb64df57c8190bc402c5ec268eaac completed May 7, 2026, 3:57 p.m.
Created at: April 9, 2026, 10:17 p.m.