Triple
T1896185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farmer |
E41986
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Ken Farmer
Ken Farmer is a notable individual recognized for his achievements and public prominence in his field.
|
E243596
|
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: Ken Farmer | Statement: [Farmer, hasNotableBearer, Ken Farmer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ken Farmer Context triple: [Farmer, hasNotableBearer, Ken Farmer]
-
A.
Neil Farmer
Neil Farmer is a British author known for his works on organizational psychology and performance improvement.
-
B.
Brian Farmer
Brian Farmer is a notable individual recognized for achievements significant enough to be distinguished among others sharing the surname Farmer.
-
C.
Mick Farmer
Mick Farmer is a notable individual distinguished enough in his field or public life to be recognized as a prominent bearer of the surname Farmer.
-
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.
Mark Farmer
Mark Farmer is a British actor best known for his roles in the television series "Grange Hill," "Minder," and "Johnny Jarvis."
- 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: Ken Farmer Triple: [Farmer, hasNotableBearer, Ken Farmer]
Generated description
Ken Farmer is a notable individual recognized for his achievements and public prominence in his field.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ken Farmer Target entity description: Ken Farmer is a notable individual recognized for his achievements and public prominence in his field.
-
A.
Neil Farmer
Neil Farmer is a British author known for his works on organizational psychology and performance improvement.
-
B.
Brian Farmer
Brian Farmer is a notable individual recognized for achievements significant enough to be distinguished among others sharing the surname Farmer.
-
C.
Mick Farmer
Mick Farmer is a notable individual distinguished enough in his field or public life to be recognized as a prominent bearer of the surname Farmer.
-
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.
Mark Farmer
Mark Farmer is a British actor best known for his roles in the television series "Grange Hill," "Minder," and "Johnny Jarvis."
- 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_69a8864b6de0819098d089f6a1b910a7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb16d6674819084a891e3bf23bd83 |
completed | March 7, 2026, 5:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5d7ffb9c8190adbe64b75bb048d4 |
completed | March 9, 2026, 5:41 a.m. |
| NEDg | Description generation | batch_69ae614cdba48190b7bd0db95e5d3aad |
completed | March 9, 2026, 5:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae61c3df388190bf8566fb8127b523 |
completed | March 9, 2026, 5:59 a.m. |
Created at: March 4, 2026, 7:35 p.m.