Triple

T11227608
Position Surface form Disambiguated ID Type / Status
Subject Antisana E265735 entity
Predicate locatedIn P40 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: [Antisana, locatedIn, Napo Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Napo Province
Context triple: [Antisana, locatedIn, 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. Sucumbíos Province
    Sucumbíos Province is an oil-rich, biodiverse region in northeastern Ecuador, located in the Amazon rainforest along the border with Colombia.
  • D. Pachitea Province
    Pachitea Province is an administrative province located in central Peru, within the Andean department of Huánuco.
  • E. Apure State
    Apure State is a largely rural and sparsely populated region in southwestern Venezuela, known for its vast Llanos plains, cattle ranching, and rich river systems that support diverse wildlife.
  • 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_69d6aac656d48190b275efaa7d6074ee completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8ff7b40819089c835be710bc575 completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4ad33fdf48190a7118c7c30577ec9 completed April 19, 2026, 10:23 a.m.
Created at: April 8, 2026, 9:30 p.m.