Triple
T14720869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sykes |
E345808
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Paul Sykes
Paul Sykes is a former English heavyweight boxer and convicted criminal who became a notorious figure in British true-crime circles.
|
E1118412
|
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: Paul Sykes | Statement: [Sykes, hasNotableBearer, Paul Sykes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Sykes Context triple: [Sykes, hasNotableBearer, Paul Sykes]
-
A.
Jonathan Sykes
Jonathan Sykes is a person notable enough to be recognized as a bearer of the surname Sykes, though specific widely known achievements or roles are not clearly established.
-
B.
Brian Molony
Brian Molony is a former Canadian bank employee whose notorious embezzlement-fueled gambling addiction became the basis for the film "Owning Mahowny."
-
C.
Drew Sykes
Drew Sykes is a film producer known for his work on the crime thriller "Emily the Criminal."
-
D.
Kian Lawley
Kian Lawley is an American YouTuber and actor known for his popular online content and roles in films and television series aimed at young adult audiences.
-
E.
Wally Sykes
Wally Sykes is a British sport shooter known for competing in international clay pigeon shooting events, including the Olympic Games.
- 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: Paul Sykes Triple: [Sykes, hasNotableBearer, Paul Sykes]
Generated description
Paul Sykes is a former English heavyweight boxer and convicted criminal who became a notorious figure in British true-crime circles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paul Sykes Target entity description: Paul Sykes is a former English heavyweight boxer and convicted criminal who became a notorious figure in British true-crime circles.
-
A.
Jonathan Sykes
Jonathan Sykes is a person notable enough to be recognized as a bearer of the surname Sykes, though specific widely known achievements or roles are not clearly established.
-
B.
Brian Molony
Brian Molony is a former Canadian bank employee whose notorious embezzlement-fueled gambling addiction became the basis for the film "Owning Mahowny."
-
C.
Drew Sykes
Drew Sykes is a film producer known for his work on the crime thriller "Emily the Criminal."
-
D.
Kian Lawley
Kian Lawley is an American YouTuber and actor known for his popular online content and roles in films and television series aimed at young adult audiences.
-
E.
Wally Sykes
Wally Sykes is a British sport shooter known for competing in international clay pigeon shooting events, including the Olympic Games.
- 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec25d56fc8190871873ca55d49272 |
completed | April 14, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0ce17f688190a86979cca8b88494 |
completed | May 8, 2026, 4:18 p.m. |
| NEDg | Description generation | batch_69fe1547d7f8819097b2bdf3b8a10751 |
completed | May 8, 2026, 4:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe15ddc6ac819098c981367b970077 |
completed | May 8, 2026, 4:57 p.m. |
Created at: April 10, 2026, 1:29 a.m.