Triple

T15069885
Position Surface form Disambiguated ID Type / Status
Subject Sofía Henríquez Bachelet E379846 entity
Predicate familyName P18 FINISHED
Object Henríquez
Henríquez is a Spanish-language surname commonly found in Latin American countries and among people of Hispanic heritage.
 E1135614 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: Henríquez | Statement: [Sofía Henríquez Bachelet, familyName, Henríquez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Henríquez
Context triple: [Sofía Henríquez Bachelet, familyName, Henríquez]
  • 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. Quiñonez
    Quiñonez is the surname of actor Tony Revolori, known for his role in "The Grand Budapest Hotel."
  • C. Vásquez
    Vásquez is a Spanish-language surname common in Latin America and Spain, borne by numerous notable figures in sports, politics, and the arts.
  • D. Carbajal
    Carbajal is a Spanish surname historically associated with figures such as Garcí Manuel de Carbajal, a notable colonial-era official.
  • E. Rojas
    Rojas is a Spanish surname historically associated with prominent noble families and political figures in Spain.
  • 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: Henríquez
Triple: [Sofía Henríquez Bachelet, familyName, Henríquez]
Generated description
Henríquez is a Spanish-language surname commonly found in Latin American countries and among people of Hispanic heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Henríquez
Target entity description: Henríquez is a Spanish-language surname commonly found in Latin American countries and among people of Hispanic heritage.
  • 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. Quiñonez
    Quiñonez is the surname of actor Tony Revolori, known for his role in "The Grand Budapest Hotel."
  • C. Vásquez
    Vásquez is a Spanish-language surname common in Latin America and Spain, borne by numerous notable figures in sports, politics, and the arts.
  • D. Carbajal
    Carbajal is a Spanish surname historically associated with figures such as Garcí Manuel de Carbajal, a notable colonial-era official.
  • E. Rojas
    Rojas is a Spanish surname historically associated with prominent noble families and political figures in Spain.
  • 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_69d85cd7683881908d405c1b5d7b4f7f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dff7f86df48190b3a2cf441fefb477 completed April 15, 2026, 8:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69fea5cd4b6c8190aa9ff73d5be31864 completed May 9, 2026, 3:11 a.m.
NEDg Description generation batch_69fea8e838b4819091e0a3d099c49059 completed May 9, 2026, 3:24 a.m.
NED2 Entity disambiguation (via description) batch_69fea986e0dc8190a56e71288c6a7ef4 completed May 9, 2026, 3:27 a.m.
Created at: April 10, 2026, 3:02 a.m.