Triple

T20511177
Position Surface form Disambiguated ID Type / Status
Subject Municipality of Apartadó E503563 entity
Predicate hasDepartmentCapital P29912 FINISHED
Object Medellín NE NERFINISHED

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: Medellín | Statement: [Municipality of Apartadó, hasDepartmentCapital, Medellín]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Medellín
Context triple: [Municipality of Apartadó, hasDepartmentCapital, Medellín]
  • A. Medellín chosen
    Medellín is Colombia’s second-largest city, known for its mountainous setting, innovative urban development, and vibrant cultural life.
  • B. Medellín
    Medellín is a historic town in the Extremadura region of western Spain, known as the birthplace of conquistador Hernán Cortés and for its well-preserved medieval castle overlooking the Guadiana River.
  • C. Bogotá and Medellín
    Bogotá and Medellín are Colombia’s two largest and most important cities, serving as major centers of politics, culture, and commerce in the country.
  • D. 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.
  • E. Manizales
    Manizales is a mountainous Colombian city known for its coffee production, cool climate, and location in the central Andes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4b2aa788190ae9eb37c1d73b1f1 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e69dcb8f5c8190b0d4c09f3669a8ec completed April 20, 2026, 9:42 p.m.
Created at: April 16, 2026, 11:36 a.m.