Triple
T2604383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Best |
E58622
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Matthew Best
Matthew Best is a notable individual distinguished enough in his field or public life to be recognized as a prominent bearer of the surname Best.
|
E283916
|
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: Matthew Best | Statement: [Best, hasNotableBearer, Matthew Best]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Best Context triple: [Best, hasNotableBearer, Matthew Best]
-
A.
Tim Best
Tim Best is a notable individual distinguished enough to be specifically recognized as a bearer of the surname Best.
-
B.
Adam Gough
Adam Gough is a British film editor known for his work on acclaimed films such as "Da 5 Bloods" and "Roma."
-
C.
Matthew Margeson
Matthew Margeson is an American film composer known for his work on action and genre films, including collaborations on scores for major Hollywood productions.
-
D.
Ian Mackley
Ian Mackley is the husband of British comedian and television personality Julian Clary.
-
E.
Jacob Best
Jacob Best was a 19th-century German-American brewer best known as the founder of what became the Pabst Brewing Company in Milwaukee, Wisconsin.
- 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: Matthew Best Triple: [Best, hasNotableBearer, Matthew Best]
Generated description
Matthew Best is a notable individual distinguished enough in his field or public life to be recognized as a prominent bearer of the surname Best.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matthew Best Target entity description: Matthew Best is a notable individual distinguished enough in his field or public life to be recognized as a prominent bearer of the surname Best.
-
A.
Tim Best
Tim Best is a notable individual distinguished enough to be specifically recognized as a bearer of the surname Best.
-
B.
Adam Gough
Adam Gough is a British film editor known for his work on acclaimed films such as "Da 5 Bloods" and "Roma."
-
C.
Matthew Margeson
Matthew Margeson is an American film composer known for his work on action and genre films, including collaborations on scores for major Hollywood productions.
-
D.
Ian Mackley
Ian Mackley is the husband of British comedian and television personality Julian Clary.
-
E.
Jacob Best
Jacob Best was a 19th-century German-American brewer best known as the founder of what became the Pabst Brewing Company in Milwaukee, Wisconsin.
- 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_69ab4ac3523881909679750c9f8c2dec |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8340eac819084eb1fe6f0ac0aa0 |
completed | March 7, 2026, 7:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af907ebc348190b1556a2104cba6f1 |
completed | March 10, 2026, 3:31 a.m. |
| NEDg | Description generation | batch_69af90f63dac8190b3282b5029d22fab |
completed | March 10, 2026, 3:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69af918e7b50819082f37f9cdb3271a2 |
completed | March 10, 2026, 3:35 a.m. |
Created at: March 6, 2026, 9:49 p.m.