Triple

T20577601
Position Surface form Disambiguated ID Type / Status
Subject Department of Antioquia E505261 entity
Predicate hasImportantCity P316 FINISHED
Object Envigado 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: Envigado | Statement: [Department of Antioquia, hasImportantCity, Envigado]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Envigado
Context triple: [Department of Antioquia, hasImportantCity, Envigado]
  • A. Envigado chosen
    Envigado is a city in northwestern Colombia that forms part of the Medellín metropolitan area and is known for its residential character and quality of life.
  • B. Vila-seca
    Vila-seca is a coastal municipality in Catalonia, Spain, known for its tourism, proximity to Tarragona, and educational facilities including a campus of Rovira i Virgili University.
  • C. Vinhedo
    Vinhedo is a municipality in southeastern Brazil known for its high quality of life, proximity to Campinas, and attractions such as the Hopi Hari theme park and annual grape festival.
  • D. Ourinhos
    Ourinhos is a municipality in the southwestern part of the state of São Paulo, Brazil, known as a regional commercial and agricultural center.
  • E. Afogados
    Afogados is a populous neighborhood in the Brazilian city of Recife, known for its busy commercial areas and dense urban character.
  • 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_69e0b4b721588190993ac7b0a9be2736 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6a90bbcfc81909ca4cab8038c634b completed April 20, 2026, 10:30 p.m.
Created at: April 16, 2026, 11:39 a.m.