Triple
T23442653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | H. Jon Benjamin as Bob Belcher |
E565443
|
entity |
| Predicate | characterHumorType |
P14479
|
FINISHED |
| Object | situationalComedy |
—
|
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: situationalComedy | Statement: [H. Jon Benjamin as Bob Belcher, characterHumorType, situationalComedy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterHumorType Context triple: [H. Jon Benjamin as Bob Belcher, characterHumorType, situationalComedy]
-
A.
hasHumorType
chosen
Indicates that an entity possesses or is characterized by a particular style, category, or type of humor.
-
B.
isHumorousCharacter
Indicates that the character is portrayed in a humorous way or primarily serves a comedic role in the context.
-
C.
humorSetting
Indicates a relationship where one entity specifies or controls the level, style, or presence of humor applied to another entity or context.
-
D.
humorousTone
Indicates that the related communication, expression, or interaction is characterized by humor, playfulness, or comedic intent.
-
E.
typeOfCharacter
Indicates that one entity is a specific kind or category of character in relation to 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64654e88190b530958b27b32412 |
completed | April 29, 2026, 6:33 a.m. |
| PD | Predicate disambiguation | batch_69f061f92da081908e7f1d0cd1e9b01c |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 5:51 p.m.