Triple
T912203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Humorous Courtier |
E19684
|
entity |
| Predicate | hasDramaticMode |
P3174
|
FINISHED |
| Object | comedy of manners |
—
|
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: comedy of manners | Statement: [The Humorous Courtier, hasDramaticMode, comedy of manners]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDramaticMode Context triple: [The Humorous Courtier, hasDramaticMode, comedy of manners]
-
A.
dramaticFunction
Indicates the role or purpose that something serves within the structure or progression of a dramatic work or narrative.
-
B.
dramaticForm
chosen
Indicates that one entity is expressed, structured, or realized in the form of a particular dramatic genre or theatrical mode.
-
C.
dramaticRole
Indicates that one entity serves as a character or part played by another entity within a dramatic or theatrical work.
-
D.
hasTorchRelay
Indicates that an event or entity includes or is associated with a torch relay as part of its activities or proceedings.
-
E.
hasFilmColorType
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
- 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_69a4939f91a08190ba68c2c81eab90fe |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b2f605bc8190a5245aa2ca55cf43 |
completed | March 1, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69a4b2918ea881908698020b995a8eae |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:39 p.m.