Triple
T17340532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two English Girls |
E421053
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Sylvia Marriott
Sylvia Marriott was a British actress known for her supporting roles in mid-20th-century film and television productions.
|
E1262389
|
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: Sylvia Marriott | Statement: [Two English Girls, castMember, Sylvia Marriott]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sylvia Marriott Context triple: [Two English Girls, castMember, Sylvia Marriott]
-
A.
Alice Marriott
Alice Marriott was an American businesswoman and co-founder of the global hospitality company Marriott International.
-
B.
Beverley Hughes
Beverley Hughes is a British Labour politician who served as a Member of Parliament and held several ministerial roles, including positions in the Home Office and as Minister for Children, Young People and Families.
-
C.
Sylvia Noble
Sylvia Noble is a recurring Doctor Who character, best known as Donna Noble’s outspoken and overprotective mother.
-
D.
Marjorie Parry
Marjorie Parry was the wife of renowned English conductor and cellist Sir John Barbirolli.
-
E.
Shirley Hodgson
Shirley Hodgson is a British clinical geneticist known for her work on inherited cancer syndromes and contributions to medical genetics research.
- 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: Sylvia Marriott Triple: [Two English Girls, castMember, Sylvia Marriott]
Generated description
Sylvia Marriott was a British actress known for her supporting roles in mid-20th-century film and television productions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sylvia Marriott Target entity description: Sylvia Marriott was a British actress known for her supporting roles in mid-20th-century film and television productions.
-
A.
Alice Marriott
Alice Marriott was an American businesswoman and co-founder of the global hospitality company Marriott International.
-
B.
Beverley Hughes
Beverley Hughes is a British Labour politician who served as a Member of Parliament and held several ministerial roles, including positions in the Home Office and as Minister for Children, Young People and Families.
-
C.
Sylvia Noble
Sylvia Noble is a recurring Doctor Who character, best known as Donna Noble’s outspoken and overprotective mother.
-
D.
Marjorie Parry
Marjorie Parry was the wife of renowned English conductor and cellist Sir John Barbirolli.
-
E.
Shirley Hodgson
Shirley Hodgson is a British clinical geneticist known for her work on inherited cancer syndromes and contributions to medical genetics research.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a15f6488190ad7d489e7391ab12 |
completed | April 19, 2026, 2:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c588a7081909ab108cb4adfedfe |
completed | May 11, 2026, 7:59 a.m. |
| NEDg | Description generation | batch_6a018e0f09c881909296656b2732bf1e |
completed | May 11, 2026, 8:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a018e7b453c81909f75593237bcf9ec |
completed | May 11, 2026, 8:08 a.m. |
Created at: April 10, 2026, 5:44 a.m.