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.