Triple
T32910309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marilyn Munster |
E841854
|
entity |
| Predicate | humorDevice |
P172889
|
FINISHED |
| Object | reversal of beauty norms |
—
|
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: reversal of beauty norms | Statement: [Marilyn Munster, humorDevice, reversal of beauty norms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: humorDevice Context triple: [Marilyn Munster, humorDevice, reversal of beauty norms]
-
A.
humorSetting
Indicates a relationship where one entity specifies or controls the level, style, or presence of humor applied to another entity or context.
-
B.
hasHumorFunction
chosen
Indicates that something serves a humorous role or purpose, such as eliciting amusement, laughter, or comedic effect.
-
C.
isHumorousCharacter
Indicates that the character is portrayed in a humorous way or primarily serves a comedic role in the context.
-
D.
humorSource
Indicates that one entity is the origin or cause of humor experienced in relation to another entity.
-
E.
humorousTone
Indicates that the related communication, expression, or interaction is characterized by humor, playfulness, or comedic intent.
- 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_69f34946a5208190bbd79f0fec4323bd |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d0d46aec819091edf97324d793ac |
completed | May 3, 2026, 4:36 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe5f93c8190995c53dbbe380a32 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 1:19 a.m.