Triple
T15457184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glyn |
E371799
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Glyn Pardoe
Glyn Pardoe was an English professional footballer best known for his long career as a defender with Manchester City during the 1960s and 1970s.
|
E1159580
|
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: Glyn Pardoe | Statement: [Glyn, hasNotableBearer, Glyn Pardoe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Glyn Pardoe Context triple: [Glyn, hasNotableBearer, Glyn Pardoe]
-
A.
Glyn Jessop
Glyn Jessop is a former English cricketer who played at the first-class level.
-
B.
Clive Tolley
Clive Tolley is a Canadian municipal politician who has served as the mayor of Moose Jaw, Saskatchewan.
-
C.
Graham Carr
Graham Carr is a Canadian academic and administrator who serves as the president of Concordia University in Montreal.
-
D.
Phil Woolpert
Phil Woolpert was a prominent American college basketball coach best known for leading the University of San Francisco to multiple national championships in the 1950s.
-
E.
Steven Pemberton
Steven Pemberton is a British computer scientist and software engineer known for his work on programming languages, web standards, and contributions to the development of ABC and early Python influences.
- 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: Glyn Pardoe Triple: [Glyn, hasNotableBearer, Glyn Pardoe]
Generated description
Glyn Pardoe was an English professional footballer best known for his long career as a defender with Manchester City during the 1960s and 1970s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Glyn Pardoe Target entity description: Glyn Pardoe was an English professional footballer best known for his long career as a defender with Manchester City during the 1960s and 1970s.
-
A.
Glyn Jessop
Glyn Jessop is a former English cricketer who played at the first-class level.
-
B.
Clive Tolley
Clive Tolley is a Canadian municipal politician who has served as the mayor of Moose Jaw, Saskatchewan.
-
C.
Graham Carr
Graham Carr is a Canadian academic and administrator who serves as the president of Concordia University in Montreal.
-
D.
Phil Woolpert
Phil Woolpert was a prominent American college basketball coach best known for leading the University of San Francisco to multiple national championships in the 1950s.
-
E.
Steven Pemberton
Steven Pemberton is a British computer scientist and software engineer known for his work on programming languages, web standards, and contributions to the development of ABC and early Python influences.
- 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f146a2c8190882741af3ec15268 |
completed | April 16, 2026, 1:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2cfbae7881909602b187e5219a35 |
completed | May 9, 2026, 12:47 p.m. |
| NEDg | Description generation | batch_69ff311546f48190b7767e4a1bb39756 |
completed | May 9, 2026, 1:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff316a855481909ff2960f66628862 |
completed | May 9, 2026, 1:06 p.m. |
Created at: April 10, 2026, 3:31 a.m.