Triple

T7185895
Position Surface form Disambiguated ID Type / Status
Subject Paipa E167569 entity
Predicate departmentCapital P18552 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: [Paipa, departmentCapital, Tunja]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tunja
Context triple: [Paipa, departmentCapital, Tunja]
  • A. Tunja chosen
    Tunja is a historic city in central Colombia known for its well-preserved colonial architecture and cultural heritage.
  • B. Manizales
    Manizales is a mountainous Colombian city known for its coffee production, cool climate, and location in the central Andes.
  • C. 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.
  • D. Montería
    Montería is a major Colombian city known as the capital of Córdoba Department, recognized for its cattle ranching economy and location along the Sinú River.
  • E. Bucaramanga
    Bucaramanga is a major city in northeastern Colombia known for its mountainous setting, pleasant climate, and role as an important commercial and industrial center.
  • 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_69c6888a7c548190a3d39b52a393080f completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e8e0f2f48190a4ddf8637f556934 completed March 27, 2026, 8:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7bf903f1c819098de137c8c43ca34 completed March 28, 2026, 11:46 a.m.
Created at: March 27, 2026, 2:49 p.m.