Triple
T8666909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norman Wisdom comedies |
E205698
|
entity |
| Predicate | typicalCharacterType |
P60013
|
FINISHED |
| Object | well-meaning but bungling man |
—
|
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: well-meaning but bungling man | Statement: [Norman Wisdom comedies, typicalCharacterType, well-meaning but bungling man]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCharacterType Context triple: [Norman Wisdom comedies, typicalCharacterType, well-meaning but bungling man]
-
A.
typeOfCharacter
chosen
Indicates that one entity is a specific kind or category of character in relation to another entity.
-
B.
typicalRole
Indicates that one entity serves as the usual, characteristic, or commonly expected role or function of another entity.
-
C.
typicalFigure
Indicates that one entity serves as a standard or representative example (a typical instance) of the other entity.
-
D.
protagonistType
Indicates the role or category that the main character (protagonist) of a story or scenario belongs to.
-
E.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
- 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_69ca83516ae88190aefe034b3bc589e3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc48a34b808190aa9aed9cdb2900e6 |
completed | March 31, 2026, 10:20 p.m. |
| PD | Predicate disambiguation | batch_69cc4564e018819081036722f3e42a71 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:31 p.m.