Triple

T3770741
Position Surface form Disambiguated ID Type / Status
Subject Napo Province E83190 entity
Predicate borders P224 FINISHED
Object Pastaza Province E49577 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: Pastaza Province | Statement: [Napo Province, borders, Pastaza Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pastaza Province
Context triple: [Napo Province, borders, Pastaza Province]
  • A. Pastaza Province chosen
    Pastaza Province is a large, sparsely populated region in eastern Ecuador within the Amazon rainforest, known for its rich biodiversity and significant Indigenous communities, including the Shuar.
  • B. Loayza Province
    Loayza Province is an administrative province located within Bolivia’s La Paz Department, known for its rural communities and Andean landscapes.
  • C. Huancané Province
    Huancané Province is an administrative division in southern Peru known for its high Andean geography and predominantly Aymara-speaking population.
  • D. Cajatambo Province
    Cajatambo Province is an administrative subdivision in the highland area of Peru’s Lima Region, known for its Andean landscapes and rural communities.
  • E. Caranavi Province
    Caranavi Province is an administrative province in Bolivia known for its coffee production and location within the Yungas region of the La Paz Department.
  • 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_69ad8b235e608190b5a2b1d1bfcef50b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcc307cf8819090730b5e697bb197 completed March 8, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5f59074e881908d346937da0b056e completed March 14, 2026, 11:56 p.m.
Created at: March 8, 2026, 3:36 p.m.