Triple

T7775092
Position Surface form Disambiguated ID Type / Status
Subject Metro de Medellín E179170 entity
Predicate serves P98 FINISHED
Object Medellín E73076 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: Medellín | Statement: [Metro de Medellín, serves, Medellín]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Medellín
Context triple: [Metro de Medellín, serves, Medellín]
  • A. Medellín chosen
    Medellín is Colombia’s second-largest city, known for its mountainous setting, innovative urban development, and vibrant cultural life.
  • B. 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.
  • C. 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.
  • D. Manizales
    Manizales is a mountainous Colombian city known for its coffee production, cool climate, and location in the central Andes.
  • E. Bogotá
    Bogotá is the high-altitude capital and largest city of Colombia, known as a major political, economic, and cultural center in South America.
  • 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_69c69f30602c819082ab52cd4af5c592 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c7046331e0819080ec1a5c23c27cd7 completed March 27, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c99ba1bbdc81909269ac0a97caa91d completed March 29, 2026, 9:37 p.m.
Created at: March 27, 2026, 4:11 p.m.