Triple

T7004920
Position Surface form Disambiguated ID Type / Status
Subject Alfonso E162427 entity
Predicate hasFeminineForm P1613 FINISHED
Object Alfonsa
Alfonsa is a feminine given name, primarily used in Romance-language cultures, derived from the masculine name Alfonso.
E634787 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: Alfonsa | Statement: [Alfonso, hasFeminineForm, Alfonsa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alfonsa
Context triple: [Alfonso, hasFeminineForm, Alfonsa]
  • A. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • B. Begoña
    Begoña is a Spanish feminine given name commonly used in Spain and Spanish-speaking countries.
  • C. 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.
  • D. 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."
  • E. Pilar
    Pilar is a riverside city in southwestern Paraguay known for its colonial architecture, river port activities, and proximity to the border with Argentina.
  • 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: Alfonsa
Triple: [Alfonso, hasFeminineForm, Alfonsa]
Generated description
Alfonsa is a feminine given name, primarily used in Romance-language cultures, derived from the masculine name Alfonso.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Alfonsa
Target entity description: Alfonsa is a feminine given name, primarily used in Romance-language cultures, derived from the masculine name Alfonso.
  • A. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • B. Begoña
    Begoña is a Spanish feminine given name commonly used in Spain and Spanish-speaking countries.
  • C. 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.
  • D. 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.
  • E. 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.
  • 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_69c6885928148190ae31909fbb5e9849 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6dc14037c81908bb87250ef29be50 completed March 27, 2026, 7:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69c76a3b2e9c8190b90c4eaaaea983ee completed March 28, 2026, 5:42 a.m.
NEDg Description generation batch_69c76b1ef6f481908f4c4f610328f633 completed March 28, 2026, 5:46 a.m.
NED2 Entity disambiguation (via description) batch_69c76c01679c8190b61f642c23c25ed5 completed March 28, 2026, 5:49 a.m.
Created at: March 27, 2026, 2:33 p.m.