Triple

T9028578
Position Surface form Disambiguated ID Type / Status
Subject Greivis Vásquez E216108 entity
Predicate familyName P18 FINISHED
Object Vásquez
Vásquez is a Spanish-language surname common in Latin America and Spain, borne by numerous notable figures in sports, politics, and the arts.
 E775657 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: Vásquez | Statement: [Greivis Vásquez, familyName, Vásquez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vásquez
Context triple: [Greivis Vásquez, familyName, Vásquez]
  • A. Velasco
    Velasco is a Spanish-origin surname borne by various notable individuals across the Spanish-speaking world and beyond.
  • B. Montúfar
    Montúfar is a Spanish-origin surname historically associated with notable figures in Latin American colonial and independence-era history.
  • C. Baquero
    Baquero is a Spanish surname most notably associated with actress Ivana Baquero, known for her role in the film "Pan's Labyrinth."
  • D. Jaramillo
    Jaramillo is a Spanish-language surname of Basque origin borne by various notable individuals across the Spanish-speaking world.
  • E. Herrera
    Herrera is a common Spanish surname borne by numerous notable figures across sports, politics, arts, and other fields in the Spanish-speaking world.
  • 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: Vásquez
Triple: [Greivis Vásquez, familyName, Vásquez]
Generated description
Vásquez is a Spanish-language surname common in Latin America and Spain, borne by numerous notable figures in sports, politics, and the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vásquez
Target entity description: Vásquez is a Spanish-language surname common in Latin America and Spain, borne by numerous notable figures in sports, politics, and the arts.
  • A. Velasco
    Velasco is a Spanish-origin surname borne by various notable individuals across the Spanish-speaking world and beyond.
  • B. Montúfar
    Montúfar is a Spanish-origin surname historically associated with notable figures in Latin American colonial and independence-era history.
  • C. Baquero
    Baquero is a Spanish surname most notably associated with actress Ivana Baquero, known for her role in the film "Pan's Labyrinth."
  • D. Jaramillo
    Jaramillo is a Spanish-language surname of Basque origin borne by various notable individuals across the Spanish-speaking world.
  • E. Herrera
    Herrera is a common Spanish surname borne by numerous notable figures across sports, politics, arts, and other fields in the Spanish-speaking world.
  • 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_69ca83a5fa88819088144801b4dd7245 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6a7fcb308190af90d6be8700e498 completed April 1, 2026, 12:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69cffda9697c81908a1a9e447519ce05 completed April 3, 2026, 5:49 p.m.
NEDg Description generation batch_69cfff51b8c0819093b2c348fd7819fe completed April 3, 2026, 5:56 p.m.
NED2 Entity disambiguation (via description) batch_69d0014b9c108190b4abe8c974677d31 completed April 3, 2026, 6:04 p.m.
Created at: March 30, 2026, 7:08 p.m.