Triple

T15932829
Position Surface form Disambiguated ID Type / Status
Subject Tena E386364 entity
Predicate isSeatOf P62 FINISHED
Object Napo Province E83190 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: Napo Province | Statement: [Tena, isSeatOf, Napo Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Napo Province
Context triple: [Tena, isSeatOf, Napo Province]
  • A. Napo Province chosen
    Napo Province is an inland region of Ecuador in the Amazon rainforest, known for its rich biodiversity, indigenous communities, and ecotourism.
  • B. Équateur Province
    Équateur Province is a region in the northwestern Democratic Republic of the Congo known for its vast rainforest, river systems, and recurring Ebola virus outbreaks.
  • C. Purús Province
    Purús Province is a remote and sparsely populated administrative division in eastern Peru, located in the Amazon rainforest near the border with Brazil.
  • D. Sucumbíos Province
    Sucumbíos Province is an oil-rich, biodiverse region in northeastern Ecuador, located in the Amazon rainforest along the border with Colombia.
  • E. Pachitea Province
    Pachitea Province is an administrative province located in central Peru, within the Andean department of Huánuco.
  • 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_69d86da750008190987eb26be3f6c118 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e156a6d9b88190b461d12d69b12ac0 completed April 16, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffbe727c348190907c9e7a5db6031d completed May 9, 2026, 11:08 p.m.
Created at: April 10, 2026, 4:53 a.m.