Triple
T1678244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Vicar of Dibley |
E36280
|
entity |
| Predicate | mainCastMember |
P5563
|
FINISHED |
| Object |
Kylie Flinker
Kylie Flinker is an actress known for her role in the British sitcom "The Vicar of Dibley."
|
E192496
|
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: Kylie Flinker | Statement: [The Vicar of Dibley, mainCastMember, Kylie Flinker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kylie Flinker Context triple: [The Vicar of Dibley, mainCastMember, Kylie Flinker]
-
A.
Katie Lucas
Katie Lucas is an American screenwriter and actress best known for her work on the animated series "Star Wars: The Clone Wars" and as the daughter of filmmaker George Lucas.
-
B.
Kailen Sheridan
Kailen Sheridan is a Canadian professional soccer goalkeeper known for her standout performances in the National Women's Soccer League and with the Canada women's national team.
-
C.
Kori Rae
Kori Rae is a film producer best known for her work at Pixar Animation Studios, including producing the animated feature "Monsters University."
-
D.
Hadley Beeman
Hadley Beeman is a web standards and technology governance expert known for her leadership within the World Wide Web Consortium (W3C) and related digital policy initiatives.
-
E.
Cydney Daly
Cydney Daly is known as the daughter of Hall of Fame NBA coach Chuck Daly.
- 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: Kylie Flinker Triple: [The Vicar of Dibley, mainCastMember, Kylie Flinker]
Generated description
Kylie Flinker is an actress known for her role in the British sitcom "The Vicar of Dibley."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kylie Flinker Target entity description: Kylie Flinker is an actress known for her role in the British sitcom "The Vicar of Dibley."
-
A.
Katie Lucas
Katie Lucas is an American screenwriter and actress best known for her work on the animated series "Star Wars: The Clone Wars" and as the daughter of filmmaker George Lucas.
-
B.
Kailen Sheridan
Kailen Sheridan is a Canadian professional soccer goalkeeper known for her standout performances in the National Women's Soccer League and with the Canada women's national team.
-
C.
Kori Rae
Kori Rae is a film producer best known for her work at Pixar Animation Studios, including producing the animated feature "Monsters University."
-
D.
Hadley Beeman
Hadley Beeman is a web standards and technology governance expert known for her leadership within the World Wide Web Consortium (W3C) and related digital policy initiatives.
-
E.
Cydney Daly
Cydney Daly is known as the daughter of Hall of Fame NBA coach Chuck Daly.
- 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_69a886139ed081909af0940aa9313512 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa625f7e1081909c3c4fe76625783a |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad8ac15c3c8190ba730217efd69a77 |
completed | March 8, 2026, 2:42 p.m. |
| NEDg | Description generation | batch_69ad9575acf88190aa3fe80794534dd4 |
completed | March 8, 2026, 3:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad97a7128c819097ff36216f00d4f9 |
completed | March 8, 2026, 3:37 p.m. |
Created at: March 4, 2026, 7:29 p.m.