Triple

T1677834
Position Surface form Disambiguated ID Type / Status
Subject Blanche E36272 entity
Predicate hasVariant P455 FINISHED
Object Blanca
Blanca is a feminine given name, common in Spanish-speaking cultures, that corresponds to the English and French name Blanche.
 E191275 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: Blanca | Statement: [Blanche, hasVariant, Blanca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Blanca
Context triple: [Blanche, hasVariant, Blanca]
  • A. Rosalinda
    Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
  • B. Rosaura
    Rosaura is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as Tita’s sister and romantic rival within the story’s intense family and culinary drama.
  • C. Mariquita
    Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • E. Consuelo
    Consuelo is a feminine given name of Spanish origin, historically associated with figures such as American socialite Consuelo Vanderbilt.
  • 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: Blanca
Triple: [Blanche, hasVariant, Blanca]
Generated description
Blanca is a feminine given name, common in Spanish-speaking cultures, that corresponds to the English and French name Blanche.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Blanca
Target entity description: Blanca is a feminine given name, common in Spanish-speaking cultures, that corresponds to the English and French name Blanche.
  • A. Rosalinda
    Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
  • B. Rosaura
    Rosaura is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as Tita’s sister and romantic rival within the story’s intense family and culinary drama.
  • C. Mariquita
    Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • E. Consuelo
    Consuelo is a feminine given name of Spanish origin, historically associated with figures such as American socialite Consuelo Vanderbilt.
  • 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_69a886139ed081909af0940aa9313512 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa625f7e1081909c3c4fe76625783a completed March 6, 2026, 5:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad798b0f1c81908174b312878e559b completed March 8, 2026, 1:28 p.m.
NEDg Description generation batch_69ad79ffb1f481908e91e87f131dd779 completed March 8, 2026, 1:30 p.m.
NED2 Entity disambiguation (via description) batch_69ad7b1a6c90819095b04638ff8a45d9 completed March 8, 2026, 1:35 p.m.
Created at: March 4, 2026, 7:29 p.m.