Triple

T11736442
Position Surface form Disambiguated ID Type / Status
Subject Mary of Burgundy E279038 entity
Predicate givenName P17 FINISHED
Object Mary
Mary of Burgundy was a 15th-century Duchess of Burgundy whose inheritance and marriage to Maximilian I of Habsburg significantly shaped the political landscape of late medieval Europe.
E943489 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: Mary | Statement: [Mary of Burgundy, givenName, Mary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary
Context triple: [Mary of Burgundy, givenName, Mary]
  • A. Mary
    Mary is the given name of the American suspense novelist Mary Higgins Clark, known for her bestselling mystery and thriller books.
  • B. Mary
    Mary is the given name of Mary Catherine Bateson, an American cultural anthropologist and writer known for her work on learning and the human life cycle.
  • C. Mary
    Mary of Lancaster was a 14th-century English noblewoman, daughter of Henry, 3rd Earl of Lancaster, and a member of the influential House of Lancaster.
  • D. Mary
    Mary is the middle name of Joseph Plunkett, the Irish nationalist, poet, and 1916 Easter Rising leader.
  • E. Mary
    Mary is the birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
  • 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: Mary
Triple: [Mary of Burgundy, givenName, Mary]
Generated description
Mary of Burgundy was a 15th-century Duchess of Burgundy whose inheritance and marriage to Maximilian I of Habsburg significantly shaped the political landscape of late medieval Europe.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mary
Target entity description: Mary of Burgundy was a 15th-century Duchess of Burgundy whose inheritance and marriage to Maximilian I of Habsburg significantly shaped the political landscape of late medieval Europe.
  • A. Mary
    Mary of Burgundy, Duchess of Savoy, was a 15th-century noblewoman from the influential Burgundian dynasty who became Duchess consort of Savoy through marriage.
  • B. Mary
    Mary was a 16th-century Habsburg archduchess who became Queen consort of Hungary and Bohemia through her marriage to King Louis II.
  • C. Mary
    Mary of Guelders was a 15th-century duchess who became Queen consort of Scotland as the wife of King James II.
  • D. Mary
    Mary of Waltham, Duchess of Brittany, was a 14th-century English princess and daughter of King Edward III who became duchess through her marriage to John IV, Duke of Brittany.
  • E. Mary
    Mary of York was a 15th-century English princess, the second daughter of King Edward IV and Elizabeth Woodville.
  • 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_69d6aaffec6881908bead509e8621742 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a4edced48190b7a59dd45921828e completed April 10, 2026, 7:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69f019b318188190bfb7effcf42974d2 completed April 28, 2026, 2:21 a.m.
NEDg Description generation batch_69f0319271788190a105828ae7582668 completed April 28, 2026, 4:03 a.m.
NED2 Entity disambiguation (via description) batch_69f05a44dcb88190a0bb57b0c8fef6b9 completed April 28, 2026, 6:57 a.m.
Created at: April 8, 2026, 9:41 p.m.