Triple

T11403276
Position Surface form Disambiguated ID Type / Status
Subject Azuay Province E270169 entity
Predicate borders P224 FINISHED
Object El Oro Province E271610 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: El Oro Province | Statement: [Azuay Province, borders, El Oro Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: El Oro Province
Context triple: [Azuay Province, borders, El Oro Province]
  • A. El Oro Province chosen
    El Oro Province is a coastal region in southwestern Ecuador known for its banana production, fishing industry, and border with Peru.
  • B. Bolívar Province
    Bolívar Province is an inland administrative region in central Ecuador, known for its Andean highland landscapes and agricultural economy.
  • C. Tequendama Province
    Tequendama Province is an administrative subdivision of the Cundinamarca Department in central Colombia, known for its Andean landscapes and proximity to Bogotá.
  • D. Carabaya Province
    Carabaya Province is an administrative division in southeastern Peru known for its high Andean landscapes, mining activities, and location within the Puno Region near the border with Bolivia.
  • E. Velasco Province
    Velasco Province is an administrative province in eastern Bolivia, located within the Santa Cruz Department and known for its vast lowland landscapes and rural communities.
  • 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_69d6aaddeaa8819088b30ef7b50598c9 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8014ab46881909fa1d425926c617b completed April 9, 2026, 7:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6684574908190bd7e3d1a7dd6d876 completed May 2, 2026, 9:10 p.m.
Created at: April 8, 2026, 9:34 p.m.