Triple

T18816763
Position Surface form Disambiguated ID Type / Status
Subject Betim plant, Minas Gerais, Brazil E460155 entity
Predicate employerIn P33603 FINISHED
Object Betim 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: Betim | Statement: [Betim plant, Minas Gerais, Brazil, employerIn, Betim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Betim
Context triple: [Betim plant, Minas Gerais, Brazil, employerIn, Betim]
  • A. Betim
    Betim is a riverside village and ferry point in North Goa, India, located across the Mandovi River from Panaji and known for its transport links and scenic waterfront.
  • B. Betim chosen
    Betim is an industrial city in southeastern Brazil known for its major automotive and petrochemical complexes.
  • C. Uberaba
    Uberaba is a mid-sized Brazilian city in the western part of Minas Gerais state, known for its strong agribusiness sector and cattle breeding traditions.
  • D. Confins
    Confins is a municipality in the Brazilian state of Minas Gerais, best known for hosting the Belo Horizonte-Confins International Airport that serves the Belo Horizonte metropolitan area.
  • E. Itanhaém
    Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
  • 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_69d8dcf94c288190a06dea029ae4b223 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5a6b620248190afa21a6ce61e2cff completed April 20, 2026, 4:08 a.m.
Created at: April 10, 2026, 11:55 a.m.