Triple

T11507617
Position Surface form Disambiguated ID Type / Status
Subject Cauca E272826 entity
Predicate hasCity P316 FINISHED
Object Patía E814312 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: Patía | Statement: [Cauca, hasCity, Patía]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Patía
Context triple: [Cauca, hasCity, Patía]
  • A. Patía chosen
    Patía is a municipality in southwestern Colombia known for its agricultural economy and location within the Andean region of the Cauca Department.
  • B. Betancuria
    Betancuria is a historic inland town and former capital of the Canary Island of Fuerteventura, known for its traditional architecture and rural charm.
  • C. Guaimaca
    Guaimaca is a town and municipality in central Honduras known for its rural character and location within the Francisco Morazán Department.
  • D. Calarcá
    Calarcá is a Colombian town and municipality in the coffee-growing Quindío Department, known for its cultural heritage and role in the Coffee Cultural Landscape.
  • E. Pasto
    Pasto is a city in southwestern Colombia known as the capital of the Nariño Department and for its rich Andean culture and traditional Black and White Carnival.
  • 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_69d6aae2c3748190bed2ea50dfb160dc completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d86db43a648190be859bec2fe9f43b completed April 10, 2026, 3:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69e624f5c9608190bde28a59860b3c93 completed April 20, 2026, 1:07 p.m.
Created at: April 8, 2026, 9:36 p.m.