Triple

T1563891
Position Surface form Disambiguated ID Type / Status
Subject Santander Department E33387 entity
Predicate hasMunicipality P847 FINISHED
Object Vélez
Vélez is a municipality in Colombia’s Santander Department known for its colonial heritage and traditional sweets.
 E180017 NE FINISHED

How this triple was built (4 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: Vélez | Statement: [Santander Department, hasMunicipality, Vélez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vélez
Context triple: [Santander Department, hasMunicipality, Vélez]
  • A. Vélez
    Vélez is a Spanish-language surname common in Latin America and Spain, borne by various notable figures in arts, sports, and public life.
  • B. Melgar
    Melgar is a popular tourist town in Colombia known for its warm climate, water parks, and proximity to major cities like Bogotá.
  • C. Durán
    Durán is an Ecuadorian city in the Guayas Province, located across the Guayas River from Guayaquil and serving as an important transport and industrial hub.
  • D. Quintero
    Quintero is a coastal Chilean city known for its beaches, port activities, and role as part of the Valparaíso Region’s industrial and tourism corridor.
  • E. Rivas
    Rivas is a city in southwestern Nicaragua known as a regional commercial center and gateway between Lake Nicaragua and the Pacific coast.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Vélez
Triple: [Santander Department, hasMunicipality, Vélez]
Generated description
Vélez is a municipality in Colombia’s Santander Department known for its colonial heritage and traditional sweets.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vélez
Target entity description: Vélez is a municipality in Colombia’s Santander Department known for its colonial heritage and traditional sweets.
  • A. Vélez
    Vélez is a Spanish-language surname common in Latin America and Spain, borne by various notable figures in arts, sports, and public life.
  • B. Melgar
    Melgar is a popular tourist town in Colombia known for its warm climate, water parks, and proximity to major cities like Bogotá.
  • C. Durán
    Durán is an Ecuadorian city in the Guayas Province, located across the Guayas River from Guayaquil and serving as an important transport and industrial hub.
  • D. Quintero
    Quintero is a coastal Chilean city known for its beaches, port activities, and role as part of the Valparaíso Region’s industrial and tourism corridor.
  • E. Rivas
    Rivas is a city in southwestern Nicaragua known as a regional commercial center and gateway between Lake Nicaragua and the Pacific coast.
  • F. None of above. chosen

Provenance (5 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_69a885ef9cf48190b0af0f5ce3d02231 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9089c7b9881909e44fee8053ac189 completed March 5, 2026, 4:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad40217be88190ae17abcf1541ec55 completed March 8, 2026, 9:23 a.m.
NEDg Description generation batch_69ad44074024819087ae57d85cb89654 completed March 8, 2026, 9:40 a.m.
NED2 Entity disambiguation (via description) batch_69ad44a1f068819095752e05a0915063 completed March 8, 2026, 9:42 a.m.
Created at: March 4, 2026, 7:27 p.m.