Triple
T10631358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Maria Theresa Villiers |
E250462
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Maria
Maria is the given name of Lady Maria Theresa Villiers, a British aristocrat from the prominent Villiers family.
|
E875213
|
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: Maria | Statement: [Lady Maria Theresa Villiers, givenName, Maria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maria Context triple: [Lady Maria Theresa Villiers, givenName, Maria]
-
A.
Maria
Maria is the protagonist of Paulo Coelho's novel "Eleven Minutes," a young Brazilian woman whose journey explores themes of love, sexuality, and self-discovery.
-
B.
Maria
Maria is the middle given name of Cesare Maria De Vecchi, an Italian Fascist politician and prominent figure in Mussolini’s regime.
-
C.
Maria
Maria is a witty and sharp-tongued gentlewoman in Olivia’s household in Shakespeare’s comedy "Twelfth Night," known for her clever schemes and playful manipulation of other characters.
-
D.
Maria
Maria is a coastal municipality on Siquijor Island in the Philippines known for its rural communities and scenic seaside landscapes.
-
E.
Maria
Maria is a key character in the historical drama "Tulip Fever," serving as the young maid whose secret romance and pregnancy help drive the film’s central scheme and emotional stakes.
- 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: Maria Triple: [Lady Maria Theresa Villiers, givenName, Maria]
Generated description
Maria is the given name of Lady Maria Theresa Villiers, a British aristocrat from the prominent Villiers family.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maria Target entity description: Maria is the given name of Lady Maria Theresa Villiers, a British aristocrat from the prominent Villiers family.
-
A.
Maria
Maria is the given name of Lady Cornelia Henrietta Maria Spencer-Churchill, a British aristocrat from the prominent Spencer-Churchill family.
-
B.
Maria
Maria is the given name of Anna Maria Spencer-Stanhope, a member of the English Spencer-Stanhope family.
-
C.
Maria
Maria is the given name of Grand Duchess Maria Alexandrovna of Russia, a 19th-century Russian imperial princess who became Duchess of Edinburgh through marriage into the British royal family.
-
D.
Maria
Maria is the given name of Maria Ludovika of Austria-Este, an Empress consort of Austria in the early 19th century.
-
E.
Maria
Maria is the given name of Maria Christina of the Netherlands, a 19th-century Dutch princess and member of the House of Orange-Nassau.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6df94dc1c8190b6347eaf35a5acc2 |
completed | April 8, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96bb4bbf08190994ea9123c0b2dab |
completed | April 10, 2026, 9:29 p.m. |
| NEDg | Description generation | batch_69d96df03c2881909af8501ecf6ac180 |
completed | April 10, 2026, 9:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d96f063d588190adcfd56b2b0afccf |
completed | April 10, 2026, 9:43 p.m. |
Created at: April 8, 2026, 9:01 p.m.