Triple

T6305329
Position Surface form Disambiguated ID Type / Status
Subject Begoña Gómez Fernández E141360 entity
Predicate givenName P17 FINISHED
Object Begoña
Begoña is a Spanish feminine given name commonly used in Spain and Spanish-speaking countries.
E585830 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: Begoña | Statement: [Begoña Gómez Fernández, givenName, Begoña]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Begoña
Context triple: [Begoña Gómez Fernández, givenName, Begoña]
  • A. Gracia Querejeta
    Gracia Querejeta is a Spanish film director and screenwriter known for her character-driven dramas and significant contributions to contemporary Spanish cinema.
  • B. Pilar
    Pilar is a Spanish feminine given name, often associated with religious devotion to Our Lady of the Pillar and traditionally used in Spain and Spanish-speaking countries.
  • C. Pilar
    Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
  • D. Pilar
    Pilar is a riverside city in southwestern Paraguay known for its colonial architecture, river port activities, and proximity to the border with Argentina.
  • E. Pilar
    Pilar is the introspective female protagonist of Paulo Coelho’s novel "By the River Piedra I Sat Down and Wept," whose spiritual and emotional journey drives the story.
  • 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: Begoña
Triple: [Begoña Gómez Fernández, givenName, Begoña]
Generated description
Begoña is a Spanish feminine given name commonly used in Spain and Spanish-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Begoña
Target entity description: Begoña is a Spanish feminine given name commonly used in Spain and Spanish-speaking countries.
  • A. Gracia Querejeta
    Gracia Querejeta is a Spanish film director and screenwriter known for her character-driven dramas and significant contributions to contemporary Spanish cinema.
  • B. Pilar
    Pilar is the introspective female protagonist of Paulo Coelho’s novel "By the River Piedra I Sat Down and Wept," whose spiritual and emotional journey drives the story.
  • C. Pilar
    Pilar is a coastal town on Siargao Island in the Philippines, known for its fishing communities and access to popular surfing and eco-tourism spots.
  • D. Pilar
    Pilar is a Spanish feminine given name, often associated with religious devotion to Our Lady of the Pillar and traditionally used in Spain and Spanish-speaking countries.
  • E. Pilar
    Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
  • 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_69c008cf0ad4819095def81e2bd42f9f completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c06479acec819090306a155a03b774 completed March 22, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69c603feee388190921239cda3772210 completed March 27, 2026, 4:13 a.m.
NEDg Description generation batch_69c6056435b481908a63a880b7bcd489 completed March 27, 2026, 4:19 a.m.
NED2 Entity disambiguation (via description) batch_69c605f2369c819080fd52282b20437e completed March 27, 2026, 4:22 a.m.
Created at: March 22, 2026, 4:28 p.m.