Triple

T7993289
Position Surface form Disambiguated ID Type / Status
Subject Mary of York E186060 entity
Predicate givenName P17 FINISHED
Object Mary
Mary of York was a 15th-century English princess, the second daughter of King Edward IV and Elizabeth Woodville.
E710995 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 York, givenName, Mary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary
Context triple: [Mary of York, givenName, Mary]
  • A. Mary
    Mary is a central figure in Christianity, venerated as the mother of Jesus and often honored as the Virgin Mary.
  • B. Mary
    Mary is the given first name of Margaret Truman, the daughter of U.S. President Harry S. Truman and a noted author and singer.
  • C. Mary
    Mary is the given name of the American suspense novelist Mary Higgins Clark, known for her bestselling mystery and thriller books.
  • D. Mary
    Mary is the middle name of Katherine Mary Dewar, a component of her full personal name.
  • E. Mary
    Mary Allerton was a Mayflower passenger and one of the early settlers of Plymouth Colony in 17th-century New England.
  • 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 York, givenName, Mary]
Generated description
Mary of York was a 15th-century English princess, the second daughter of King Edward IV and Elizabeth Woodville.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mary
Target entity description: Mary of York was a 15th-century English princess, the second daughter of King Edward IV and Elizabeth Woodville.
  • A. 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.
  • 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 Burgundy, Duchess of Savoy, was a 15th-century noblewoman from the influential Burgundian dynasty who became Duchess consort of Savoy through marriage.
  • D. Mary
    Mary, Princess Royal and Countess of Harewood, was a daughter of King George V and Queen Mary of the United Kingdom and a prominent British royal figure in the early to mid-20th century.
  • E. Mary
    Mary, Princess Royal and Princess of Orange, was the eldest daughter of King Charles I of England and the wife of William II of Orange, making her a key figure in 17th-century Anglo-Dutch royal relations.
  • 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_69ca829c6c308190ab05b43d234c52b2 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c729afc81909d477b1623ac3f9d completed March 31, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc63bcae048190a3fd151b2d8f9f77 completed April 1, 2026, 12:15 a.m.
NEDg Description generation batch_69cc64bd6a088190b77e2709c76579e4 completed April 1, 2026, 12:20 a.m.
NED2 Entity disambiguation (via description) batch_69cc66b0e1548190840e4335ff2b130f completed April 1, 2026, 12:28 a.m.
Created at: March 30, 2026, 5:16 p.m.