Triple

T819532
Position Surface form Disambiguated ID Type / Status
Subject Margaret E17722 entity
Predicate hasVariant P455 FINISHED
Object Margareta
Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
 E113357 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: Margareta | Statement: [Margaret, hasVariant, Margareta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Margareta
Context triple: [Margaret, hasVariant, Margareta]
  • A. 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.
  • B. Astrid
    Astrid is a Belgian princess and member of the country’s royal family.
  • C. Johanna
    Johanna is the given name of Johanna Spyri, the Swiss author best known for creating the classic children's novel "Heidi."
  • D. Joanna
    Joanna is the first name of Joanna Newsom, an American harpist, singer-songwriter, and musician known for her intricate compositions and distinctive vocal style.
  • E. Franziska
    Franziska is a feminine given name of German origin, closely related to and cognate with the 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: Margareta
Triple: [Margaret, hasVariant, Margareta]
Generated description
Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Margareta
Target entity description: Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
  • A. 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.
  • B. Astrid
    Astrid is a Belgian princess and member of the country’s royal family.
  • C. Johanna
    Johanna is the given name of Johanna Spyri, the Swiss author best known for creating the classic children's novel "Heidi."
  • D. Joanna
    Joanna is the first name of Joanna Newsom, an American harpist, singer-songwriter, and musician known for her intricate compositions and distinctive vocal style.
  • E. Franziska
    Franziska is a feminine given name of German origin, closely related to and cognate with the 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_69ac11907a988190945e5e5ec8a26ccf completed March 7, 2026, 11:52 a.m.
NEDg Description generation batch_69ac12f09744819084a8486e157db474 completed March 7, 2026, 11:58 a.m.
NED2 Entity disambiguation (via description) batch_69ac1342004881908b9da2192e1aad3f completed March 7, 2026, noon
Created at: March 1, 2026, 7:38 p.m.