Triple

T7516004
Position Surface form Disambiguated ID Type / Status
Subject Floridablanca E177644 entity
Predicate hasTransportConnection P845 FINISHED
Object Bucaramanga E187310 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: Bucaramanga | Statement: [Floridablanca, hasTransportConnection, Bucaramanga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bucaramanga
Context triple: [Floridablanca, hasTransportConnection, Bucaramanga]
  • A. Bucaramanga chosen
    Bucaramanga is a major city in northeastern Colombia known for its mountainous setting, pleasant climate, and role as an important commercial and industrial center.
  • 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. Tunja
    Tunja is a historic city in central Colombia known for its well-preserved colonial architecture and cultural heritage.
  • D. Manizales
    Manizales is a mountainous Colombian city known for its coffee production, cool climate, and location in the central Andes.
  • 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_69c69f2891148190a484f3b8222c6f1b completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f5f595148190b36649b0095bb898 completed March 27, 2026, 9:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8b4e9b3348190a24823ff9ad3431e completed March 29, 2026, 5:13 a.m.
Created at: March 27, 2026, 3:45 p.m.