Triple

T819531
Position Surface form Disambiguated ID Type / Status
Subject Margaret E17722 entity
Predicate hasVariant P455 FINISHED
Object Margrete
Margrete is a Scandinavian variant of the female given name Margaret, commonly used in Nordic countries.
 E98326 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: Margrete | Statement: [Margaret, hasVariant, Margrete]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Margrete
Context triple: [Margaret, hasVariant, Margrete]
  • A. Queen Bavmorda
    Queen Bavmorda is the ruthless and power-hungry sorceress-queen who serves as the primary villain in the fantasy film "Willow."
  • B. Isabella
    Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
  • C. Kœnig
    Kœnig is a French surname most notably associated with figures such as General Marie-Pierre Kœnig, a prominent military leader during World War II.
  • D. Beatrix
    Beatrix is the former Queen of the Netherlands who reigned from 1980 until her abdication in 2013.
  • E. König
    König is a German-language surname borne by numerous individuals, including notable figures in fields such as religion, science, and the arts.
  • 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: Margrete
Triple: [Margaret, hasVariant, Margrete]
Generated description
Margrete is a Scandinavian variant of the female given name Margaret, commonly used in Nordic countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Margrete
Target entity description: Margrete is a Scandinavian variant of the female given name Margaret, commonly used in Nordic countries.
  • A. Queen Bavmorda
    Queen Bavmorda is the ruthless and power-hungry sorceress-queen who serves as the primary villain in the fantasy film "Willow."
  • B. Isabella
    Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
  • C. Kœnig
    Kœnig is a French surname most notably associated with figures such as General Marie-Pierre Kœnig, a prominent military leader during World War II.
  • D. Beatrix
    Beatrix is the former Queen of the Netherlands who reigned from 1980 until her abdication in 2013.
  • E. König
    König is a German-language surname borne by numerous individuals, including notable figures in fields such as religion, science, and the arts.
  • 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_69a76d8f639081909690d1ef4c98680e completed March 3, 2026, 11:23 p.m.
NEDg Description generation batch_69a781f5536c81908175d58b6b75adba completed March 4, 2026, 12:51 a.m.
NED2 Entity disambiguation (via description) batch_69a7860e656c8190a08a9999662ba1f1 completed March 4, 2026, 1:08 a.m.
Created at: March 1, 2026, 7:38 p.m.