Triple
T1842801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howells |
E41212
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Gareth Howells
Gareth Howells is a Welsh former professional football goalkeeper who played in the English Football League during the late 20th century.
|
E209102
|
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: Gareth Howells | Statement: [Howells, hasNotableBearer, Gareth Howells]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gareth Howells Context triple: [Howells, hasNotableBearer, Gareth Howells]
-
A.
Gareth David-Lloyd
Gareth David-Lloyd is a Welsh actor best known for playing Ianto Jones in the Doctor Who spin-off series Torchwood.
-
B.
Gareth Hunt
Gareth Hunt was a British actor best known for his roles in the television series "The New Avengers" and the sitcom "Side by Side."
-
C.
Gareth Unwin
Gareth Unwin is a British film producer best known for his Academy Award-winning work on the historical drama "The King’s Speech."
-
D.
Graydon Hoare
Graydon Hoare is a Canadian software developer best known as the original creator of the Rust programming language.
-
E.
James Abercrombie
James Abercrombie was a British Army officer who fought in the early campaigns of the American Revolutionary War, including the Battle of Bunker Hill.
- 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: Gareth Howells Triple: [Howells, hasNotableBearer, Gareth Howells]
Generated description
Gareth Howells is a Welsh former professional football goalkeeper who played in the English Football League during the late 20th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gareth Howells Target entity description: Gareth Howells is a Welsh former professional football goalkeeper who played in the English Football League during the late 20th century.
-
A.
Gareth David-Lloyd
Gareth David-Lloyd is a Welsh actor best known for playing Ianto Jones in the Doctor Who spin-off series Torchwood.
-
B.
Gareth Hunt
Gareth Hunt was a British actor best known for his roles in the television series "The New Avengers" and the sitcom "Side by Side."
-
C.
Gareth Unwin
Gareth Unwin is a British film producer best known for his Academy Award-winning work on the historical drama "The King’s Speech."
-
D.
Graydon Hoare
Graydon Hoare is a Canadian software developer best known as the original creator of the Rust programming language.
-
E.
James Abercrombie
James Abercrombie was a British Army officer who fought in the early campaigns of the American Revolutionary War, including the Battle of Bunker Hill.
- 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_69a88648cd44819093303206d96d76ad |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb04eb0748190b226f932e544925f |
completed | March 7, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69addf48e3048190a79824fd92e0079f |
completed | March 8, 2026, 8:42 p.m. |
| NEDg | Description generation | batch_69ade0158ca8819090e630d87d5f3947 |
completed | March 8, 2026, 8:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ade07bd83c819083120092d672793e |
completed | March 8, 2026, 8:47 p.m. |
Created at: March 4, 2026, 7:33 p.m.