Triple
T17374296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Bishop |
E422394
|
entity |
| Predicate | hasGivenOccupationAs |
P124115
|
FINISHED |
| Object | stand-up comedian |
—
|
LITERAL 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: stand-up comedian | Statement: [John Bishop, hasGivenOccupationAs, stand-up comedian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGivenOccupationAs Context triple: [John Bishop, hasGivenOccupationAs, stand-up comedian]
-
A.
hasGivenProfession
chosen
Indicates that an entity holds or practices a specified profession or occupation.
-
B.
endedOccupationOf
Indicates that one entity brought another entity’s occupation or control of a place or position to an end.
-
C.
hasPastOccupation
Indicates that an entity previously held a particular job, role, or occupation in the past.
-
D.
hasTypicalOccupation
Indicates that an entity commonly or characteristically works in a particular job or profession.
-
E.
hasHumanOccupationEvidence
Indicates that there is supporting evidence that a human has held or currently holds a particular occupation or job role.
- F. None of above.
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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a6b71148190bb10e1fac400d6c3 |
completed | April 19, 2026, 2:14 a.m. |
| PD | Predicate disambiguation | batch_69e3b02ac8688190a7182f1b2151d721 |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:44 a.m.