Triple
T16526742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Big Breakfast |
E401459
|
entity |
| Predicate | presenter |
P83
|
FINISHED |
| Object |
Johnny Vaughan
Johnny Vaughan is a British television and radio presenter and broadcaster best known for his energetic, humorous style on UK entertainment shows.
|
E1218033
|
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: Johnny Vaughan | Statement: [The Big Breakfast, presenter, Johnny Vaughan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Johnny Vaughan Context triple: [The Big Breakfast, presenter, Johnny Vaughan]
-
A.
Rocky Vaughan
Rocky Vaughan is an American graphic designer best known for creating the design that became Mississippi’s current state flag.
-
B.
Roy Marples
Roy Marples is a software engineer best known for his work on the OpenRC init system and various networking tools in the Linux and BSD ecosystems.
-
C.
Lee Boardman
Lee Boardman is a British actor known for his roles in television dramas such as Rome and Coronation Street.
-
D.
Tony Wane
Tony Wane was an actor known for his role in the 1935 British film "Sanders of the River."
-
E.
Ronald Drever
Ronald Drever was a Scottish experimental physicist best known as a co-founder of the Laser Interferometer Gravitational-Wave Observatory (LIGO) and a pioneer in the detection of gravitational waves.
- 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: Johnny Vaughan Triple: [The Big Breakfast, presenter, Johnny Vaughan]
Generated description
Johnny Vaughan is a British television and radio presenter and broadcaster best known for his energetic, humorous style on UK entertainment shows.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Johnny Vaughan Target entity description: Johnny Vaughan is a British television and radio presenter and broadcaster best known for his energetic, humorous style on UK entertainment shows.
-
A.
Rocky Vaughan
Rocky Vaughan is an American graphic designer best known for creating the design that became Mississippi’s current state flag.
-
B.
Roy Marples
Roy Marples is a software engineer best known for his work on the OpenRC init system and various networking tools in the Linux and BSD ecosystems.
-
C.
Lee Boardman
Lee Boardman is a British actor known for his roles in television dramas such as Rome and Coronation Street.
-
D.
Tony Wane
Tony Wane was an actor known for his role in the 1935 British film "Sanders of the River."
-
E.
Ronald Drever
Ronald Drever was a Scottish experimental physicist best known as a co-founder of the Laser Interferometer Gravitational-Wave Observatory (LIGO) and a pioneer in the detection of gravitational waves.
- 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_69d883838abc8190bc79cb2d41733ce2 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32ed4b8a08190b5f179fc583001a6 |
completed | April 18, 2026, 7:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00608d36dc8190a094fa4513147c85 |
completed | May 10, 2026, 10:40 a.m. |
| NEDg | Description generation | batch_6a0061a6e1e88190a5efe0430db0bd9b |
completed | May 10, 2026, 10:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00627908988190803707069872e4c5 |
completed | May 10, 2026, 10:48 a.m. |
Created at: April 10, 2026, 5:14 a.m.