Triple
T2463695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bonnie Swanson |
E55191
|
entity |
| Predicate | humorType |
P14479
|
FINISHED |
| Object | dark humor situations |
—
|
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: dark humor situations | Statement: [Bonnie Swanson, humorType, dark humor situations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: humorType Context triple: [Bonnie Swanson, humorType, dark humor situations]
-
A.
hasHumorType
chosen
Indicates that an entity possesses or is characterized by a particular style, category, or type of humor.
-
B.
parodies
Indicates that one entity imitates another in an exaggerated or humorous way, often to criticize or comment on the original.
-
C.
entertainmentType
Indicates the kind or category of entertainment associated with an entity or event.
-
D.
genreOfQuotes
Indicates that one entity is the literary, thematic, or stylistic genre to which the other entity’s quotes belong.
-
E.
typicalVariety
Indicates that one entity is a representative or characteristic example of the variety or type defined by another entity.
- 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_69ab49e3622c8190ad22afa2c4fbb807 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd2bc7b5481908b3664495e99f1a4 |
completed | March 7, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69abd0b3ea308190a6d8499c2a542c50 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:44 p.m.