Triple

T19366157
Position Surface form Disambiguated ID Type / Status
Subject Mary of France, daughter of Philip II and Agnes E484406 entity
Predicate givenName P17 FINISHED
Object Mary
Mary of France was a medieval French princess, daughter of King Philip II and Agnes of Merania.
E1375560 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 France, daughter of Philip II and Agnes, givenName, Mary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary
Context triple: [Mary of France, daughter of Philip II and Agnes, givenName, Mary]
  • A. Mary
    Mary is the middle name of Edith Tolkien, the wife of author J.R.R. Tolkien.
  • B. Mary
    Mary is the birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
  • C. Mary
    Mary is the given name of the American stage and film actress Josephine Hull, known for her roles in classic mid-20th-century theater and cinema.
  • D. Mary
    Mary is a character in the "Tunnel community" setting, known as one of the individuals living within its underground society.
  • E. Mary
    Mary is the given first name of American actress, author, and radio host Marilu Henner.
  • 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 France, daughter of Philip II and Agnes, givenName, Mary]
Generated description
Mary of France was a medieval French princess, daughter of King Philip II and Agnes of Merania.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mary
Target entity description: Mary of France was a medieval French princess, daughter of King Philip II and Agnes of Merania.
  • A. Mary
    Mary of York was a 15th-century English princess, the second daughter of King Edward IV and Elizabeth Woodville.
  • B. 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.
  • C. Mary
    Mary was a 16th-century Habsburg archduchess who became Queen consort of Hungary and Bohemia through her marriage to King Louis II.
  • D. 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.
  • E. 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.
  • 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_69d8e8d305088190ad13571532aa454c completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e619ac26d4819095836d737b629cf1 completed April 20, 2026, 12:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0733bfb3dc81908fe01c43583d0a57 completed May 15, 2026, 2:54 p.m.
NEDg Description generation batch_6a073566dfd8819097e5cee714ccebda completed May 15, 2026, 3:01 p.m.
NED2 Entity disambiguation (via description) batch_6a0735b445388190a588f6eeaf49e1b7 completed May 15, 2026, 3:03 p.m.
Created at: April 10, 2026, 1:35 p.m.