Triple

T14841427
Position Surface form Disambiguated ID Type / Status
Subject SKEMA Business School E348973 entity
Predicate hasCampusIn P4623 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: [SKEMA Business School, hasCampusIn, Belo Horizonte]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Belo Horizonte
Context triple: [SKEMA Business School, hasCampusIn, 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. Goiânia
    Goiânia is the capital and largest city of the Brazilian state of Goiás, known as a major regional center for agriculture, industry, and services in central Brazil.
  • D. Guarulhos
    Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
  • E. 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.
  • 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_69d822ec69008190a9232caa68836872 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded28fa49c81908d1059e6cafd607f completed April 14, 2026, 11:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe7e7de2c08190abf827376cd415ba completed May 9, 2026, 12:23 a.m.
Created at: April 10, 2026, 1:53 a.m.