Triple
T2604375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Best |
E58622
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Tim Best
Tim Best is a notable individual distinguished enough to be specifically recognized as a bearer of the surname Best.
|
E281134
|
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: Tim Best | Statement: [Best, hasNotableBearer, Tim Best]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tim Best Context triple: [Best, hasNotableBearer, Tim Best]
-
A.
Tim Besse
Tim Besse is an American entrepreneur best known as a co-founder of the employee review and job-listing platform Glassdoor.
-
B.
Graeme Revell
Graeme Revell is a New Zealand-born composer best known for his atmospheric film scores across genres including horror, action, and science fiction.
-
C.
Mark Carlisle
Mark Carlisle was a British Conservative politician who served in senior government roles, including as Secretary of State for Education and Science under Prime Minister Margaret Thatcher.
-
D.
Christopher Le Brun
Christopher Le Brun is a British painter, sculptor, and printmaker who served as President of the Royal Academy of Arts in London.
-
E.
Peter Sissons
Peter Sissons was a prominent British journalist and television newsreader best known for presenting major news programmes on the BBC and ITN.
- 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: Tim Best Triple: [Best, hasNotableBearer, Tim Best]
Generated description
Tim Best is a notable individual distinguished enough to be specifically recognized as a bearer of the surname Best.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tim Best Target entity description: Tim Best is a notable individual distinguished enough to be specifically recognized as a bearer of the surname Best.
-
A.
Tim Besse
Tim Besse is an American entrepreneur best known as a co-founder of the employee review and job-listing platform Glassdoor.
-
B.
Graeme Revell
Graeme Revell is a New Zealand-born composer best known for his atmospheric film scores across genres including horror, action, and science fiction.
-
C.
Mark Carlisle
Mark Carlisle was a British Conservative politician who served in senior government roles, including as Secretary of State for Education and Science under Prime Minister Margaret Thatcher.
-
D.
Christopher Le Brun
Christopher Le Brun is a British painter, sculptor, and printmaker who served as President of the Royal Academy of Arts in London.
-
E.
Peter Sissons
Peter Sissons was a prominent British journalist and television newsreader best known for presenting major news programmes on the BBC and ITN.
- 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_69af83dac58c81908be66bf40c810e3c |
completed | March 10, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_69af846ab5888190aa04ee83752208e2 |
completed | March 10, 2026, 2:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69af84e909308190a6a1a2e818f263c4 |
completed | March 10, 2026, 2:41 a.m. |
Created at: March 6, 2026, 9:49 p.m.