Triple
T7904801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baby Herman |
E183544
|
entity |
| Predicate | comedicDevice |
P63937
|
FINISHED |
| Object | contrast between baby appearance and adult personality |
—
|
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: contrast between baby appearance and adult personality | Statement: [Baby Herman, comedicDevice, contrast between baby appearance and adult personality]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: comedicDevice Context triple: [Baby Herman, comedicDevice, contrast between baby appearance and adult personality]
-
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.
isHumorousCharacter
Indicates that the character is portrayed in a humorous way or primarily serves a comedic role in the context.
-
C.
hasComedyElements
Indicates that something contains humorous or comedic aspects as part of its overall content or style.
-
D.
notableGag
Indicates that something features a particularly memorable or significant joke, comedic moment, or running gag.
-
E.
humorSource
chosen
Indicates that one entity is the origin or cause of humor experienced 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a4331cc8190b50301c78767a850 |
completed | March 31, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:03 p.m.