Triple
T25831792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How to Break into Show Business (comedy routines) |
E650682
|
entity |
| Predicate | hasHumorDevice |
P114828
|
FINISHED |
| Object | double meaning |
—
|
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: double meaning | Statement: [How to Break into Show Business (comedy routines), hasHumorDevice, double meaning]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHumorDevice Context triple: [How to Break into Show Business (comedy routines), hasHumorDevice, double meaning]
-
A.
hasHumorType
Indicates that an entity possesses or is characterized by a particular style, category, or type of humor.
-
B.
humorSetting
Indicates a relationship where one entity specifies or controls the level, style, or presence of humor applied to another entity or context.
-
C.
isHumorousCharacter
Indicates that the character is portrayed in a humorous way or primarily serves a comedic role in the context.
-
D.
hasComedyElements
Indicates that something contains humorous or comedic aspects as part of its overall content or style.
-
E.
usedForHumor
chosen
Indicates that something is employed with the intention of being funny, amusing, or comical.
- 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_69e7ab37438081908f1ccf6284839520 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f6135293908190809e255bf6334760 |
completed | May 2, 2026, 3:08 p.m. |
| PD | Predicate disambiguation | batch_69f611a72780819082f44e66ca2c6ac9 |
completed | May 2, 2026, 3 p.m. |
Created at: April 22, 2026, 7:38 a.m.