Triple

T2103535
Position Surface form Disambiguated ID Type / Status
Subject Albuquerque E37142 entity
Predicate hasNickname P39 FINISHED
Object Burque
Burque is a colloquial nickname commonly used to refer to the city of Albuquerque, New Mexico.
 E237330 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: Burque | Statement: [Albuquerque, hasNickname, Burque]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Burque
Context triple: [Albuquerque, hasNickname, Burque]
  • A. Herrera
    Herrera is a common Spanish surname borne by numerous notable figures across sports, politics, arts, and other fields in the Spanish-speaking world.
  • B. Esquivel
    Esquivel is a Spanish-language surname borne by various notable figures in literature, politics, and the arts across Latin America.
  • C. Echeverría
    Echeverría is a Spanish-language surname borne by various notable figures in politics, literature, and the arts across the Spanish-speaking world.
  • D. Simón
    Simón is the given name of Simón Bolívar, the famed Latin American military and political leader who played a key role in the independence of several South American countries from Spanish rule.
  • E. Andrés
    Andrés is a Spanish given name commonly used as the equivalent of Andrew.
  • 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: Burque
Triple: [Albuquerque, hasNickname, Burque]
Generated description
Burque is a colloquial nickname commonly used to refer to the city of Albuquerque, New Mexico.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Burque
Target entity description: Burque is a colloquial nickname commonly used to refer to the city of Albuquerque, New Mexico.
  • A. Herrera
    Herrera is a common Spanish surname borne by numerous notable figures across sports, politics, arts, and other fields in the Spanish-speaking world.
  • B. Esquivel
    Esquivel is a Spanish-language surname borne by various notable figures in literature, politics, and the arts across Latin America.
  • C. Echeverría
    Echeverría is a Spanish-language surname borne by various notable figures in politics, literature, and the arts across the Spanish-speaking world.
  • D. Simón
    Simón is the given name of Simón Bolívar, the famed Latin American military and political leader who played a key role in the independence of several South American countries from Spanish rule.
  • E. Andrés
    Andrés is a Spanish given name commonly used as the equivalent of Andrew.
  • 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_69a8861828948190924aa30c08806b3a completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abbabf7cdc81909636dff34badc1c5 completed March 7, 2026, 5:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae518fb0b4819096a8ce455e22661a completed March 9, 2026, 4:50 a.m.
NEDg Description generation batch_69ae523cdebc819088b94e67b5311527 completed March 9, 2026, 4:53 a.m.
NED2 Entity disambiguation (via description) batch_69ae52c56c5c8190bbdd2af3dde63374 completed March 9, 2026, 4:55 a.m.
Created at: March 4, 2026, 7:43 p.m.