Triple

T819536
Position Surface form Disambiguated ID Type / Status
Subject Margaret E17722 entity
Predicate hasVariant P455 FINISHED
Object Margarida
Margarida is a given name, commonly used in Portuguese and Catalan, that corresponds to the English name Margaret.
 E114896 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: Margarida | Statement: [Margaret, hasVariant, Margarida]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Margarida
Context triple: [Margaret, hasVariant, Margarida]
  • A. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • B. Gertrudis
    Gertrudis is a passionate and rebellious sister in "Like Water for Chocolate" whose fiery nature and unconventional choices challenge her family's strict traditions.
  • C. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • D. Caterina
    Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
  • E. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • 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: Margarida
Triple: [Margaret, hasVariant, Margarida]
Generated description
Margarida is a given name, commonly used in Portuguese and Catalan, that corresponds to the English name Margaret.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Margarida
Target entity description: Margarida is a given name, commonly used in Portuguese and Catalan, that corresponds to the English name Margaret.
  • A. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • B. Gertrudis
    Gertrudis is a passionate and rebellious sister in "Like Water for Chocolate" whose fiery nature and unconventional choices challenge her family's strict traditions.
  • C. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • D. Caterina
    Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
  • E. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • 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_69a4937bcaac8190a322524ac6f45a5a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4ab656418819091ecb09e7ede2825 completed March 1, 2026, 9:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac16f56bc0819094085d61f1f29f70 completed March 7, 2026, 12:15 p.m.
NEDg Description generation batch_69ac1841a6188190bca3ab98eb169d47 completed March 7, 2026, 12:21 p.m.
NED2 Entity disambiguation (via description) batch_69ac18afee148190ac7431327588c31b completed March 7, 2026, 12:23 p.m.
Created at: March 1, 2026, 7:38 p.m.