Triple

T1557267
Position Surface form Disambiguated ID Type / Status
Subject Lupe Vélez E33234 entity
Predicate familyName P18 FINISHED
Object Vélez
Vélez is a Spanish-language surname common in Latin America and Spain, borne by various notable figures in arts, sports, and public life.
 E177187 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: [Lupe Vélez, familyName, Vélez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vélez
Context triple: [Lupe Vélez, familyName, Vélez]
  • A. Melgar
    Melgar is a popular tourist town in Colombia known for its warm climate, water parks, and proximity to major cities like Bogotá.
  • B. 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.
  • C. 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.
  • D. Rivas
    Rivas is a city in southwestern Nicaragua known as a regional commercial center and gateway between Lake Nicaragua and the Pacific coast.
  • E. Tolimense
    Tolimense is the Spanish demonym for people from the Tolima Department in central Colombia, reflecting the region’s Andean culture and traditions.
  • 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: [Lupe Vélez, familyName, Vélez]
Generated description
Vélez is a Spanish-language surname common in Latin America and Spain, borne by various notable figures in arts, sports, and public life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vélez
Target entity description: Vélez is a Spanish-language surname common in Latin America and Spain, borne by various notable figures in arts, sports, and public life.
  • A. Melgar
    Melgar is a popular tourist town in Colombia known for its warm climate, water parks, and proximity to major cities like Bogotá.
  • B. 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.
  • C. 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.
  • D. Rivas
    Rivas is a city in southwestern Nicaragua known as a regional commercial center and gateway between Lake Nicaragua and the Pacific coast.
  • E. Tolimense
    Tolimense is the Spanish demonym for people from the Tolima Department in central Colombia, reflecting the region’s Andean culture and traditions.
  • 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_69a908704d208190937af41c6454df4e completed March 5, 2026, 4:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad3710688c8190a280f03bced5601f completed March 8, 2026, 8:45 a.m.
NEDg Description generation batch_69ad3812855881909b571d6a7a96d524 completed March 8, 2026, 8:49 a.m.
NED2 Entity disambiguation (via description) batch_69ad38a4017c8190b2c5f1f0d1d9b5a5 completed March 8, 2026, 8:51 a.m.
Created at: March 4, 2026, 7:27 p.m.