Triple

T7796184
Position Surface form Disambiguated ID Type / Status
Subject José María Córdova International Airport E180303 entity
Predicate servesCity P82 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: [José María Córdova International Airport, servesCity, Medellín]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Medellín
Context triple: [José María Córdova International Airport, servesCity, 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_69ca827d22208190b4dc5aa680edcf5d completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cae94c41408190b73e37c0ff2c6628 completed March 30, 2026, 9:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69cbdeb6c1b88190a38fb4507bfb380c completed March 31, 2026, 2:48 p.m.
Created at: March 30, 2026, 4:31 p.m.