Triple

T1459313
Position Surface form Disambiguated ID Type / Status
Subject Boyacá Department E31473 entity
Predicate hasCity P316 FINISHED
Object Tunja E167566 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: Tunja | Statement: [Boyacá Department, hasCity, Tunja]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tunja
Context triple: [Boyacá Department, hasCity, Tunja]
  • A. Tunja chosen
    Tunja is a historic city in central Colombia known for its well-preserved colonial architecture and cultural heritage.
  • B. Apartadó
    Apartadó is a municipality in Colombia’s Antioquia Department, known as an important agricultural and commercial center in the Urabá region, especially for banana production.
  • C. Sogamoso
    Sogamoso is a Colombian city in the Andean region known historically as a major religious and cultural center of the Muisca civilization and today for its industry and mining.
  • D. Anapoima
    Anapoima is a warm-climate resort town and popular weekend getaway located in the Cundinamarca department of central Colombia.
  • E. Medellín
    Medellín is Colombia’s second-largest city, known for its mountainous setting, innovative urban development, and vibrant cultural life.
  • 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_69a49917dfc081909acdbdf5d684f1ef completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c59c1c288190be08064f2d351b2b completed March 1, 2026, 11:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad15a299ac81908f37085a107c8e9f completed March 8, 2026, 6:22 a.m.
Created at: March 1, 2026, 8 p.m.