Triple
T10241256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Farmer |
E243596
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ken Farmer |
E243596
|
NE FINISHED |
How this triple was built (2 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: [Ken Farmer, name, Ken Farmer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ken Farmer Context triple: [Ken Farmer, name, Ken Farmer]
-
A.
Ken Farmer
chosen
Ken Farmer is a notable individual recognized for his achievements and public prominence in his field.
-
B.
Neil Farmer
Neil Farmer is a British author known for his works on organizational psychology and performance improvement.
-
C.
Martin Boddey
Martin Boddey was a British character actor known for his frequent supporting roles in mid-20th-century films and television, often portraying authority figures such as policemen and officials.
-
D.
Gil Shepherd
Gil Shepherd is a fictional 1930s movie star character in Woody Allen’s film "The Purple Rose of Cairo," whose on-screen persona steps into the real world, blurring the line between cinema and reality.
-
E.
Brian Farmer
Brian Farmer is a notable individual recognized for achievements significant enough to be distinguished among others sharing the surname Farmer.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d21f2ae0819098ac60c828dc9cae |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71cb24d088190848d2fb1fffe4c48 |
completed | April 9, 2026, 3:27 a.m. |
Created at: April 6, 2026, 11:24 a.m.