Triple

T5689368
Position Surface form Disambiguated ID Type / Status
Subject Brasília Time E125390 entity
Predicate observedIn P40 FINISHED
Object Belo Horizonte E112920 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: Belo Horizonte | Statement: [Brasília Time, observedIn, Belo Horizonte]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Belo Horizonte
Context triple: [Brasília Time, observedIn, Belo Horizonte]
  • A. Belo Horizonte chosen
    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.
  • B. São Paulo
    São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
  • C. Guarulhos
    Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
  • D. Juiz de Fora
    Juiz de Fora is a major industrial and university city in the state of Minas Gerais, known for its strategic location between Rio de Janeiro, São Paulo, and Belo Horizonte.
  • E. Brasília
    Brasília is the modernist-planned capital city of Brazil, known for its distinctive architecture and role as a major political and administrative center in South America.
  • 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_69c0082bb19c8190823a4facd3cba79b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c023e1c6148190aeae7620bd9ee9d4 completed March 22, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0b0ade6648190b29c64f83ceed326 completed March 23, 2026, 3:17 a.m.
Created at: March 22, 2026, 3:44 p.m.