Triple
T18334860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Larry Arbogast |
E439243
|
entity |
| Predicate | hasHumorousRole |
P71317
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Dr. Larry Arbogast, hasHumorousRole, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHumorousRole Context triple: [Dr. Larry Arbogast, hasHumorousRole, yes]
-
A.
isHumorousCharacter
chosen
Indicates that the character is portrayed in a humorous way or primarily serves a comedic role in the context.
-
B.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
C.
hasHumorType
Indicates that an entity possesses or is characterized by a particular style, category, or type of humor.
-
D.
hasCastMemberPlayingThemself
Indicates that a work includes a cast member who appears on screen portraying themself rather than a fictional character.
-
E.
hasComedyElements
Indicates that something contains humorous or comedic aspects as part of its overall content or style.
- 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_69d8b9175fec8190af865699b4e64d8c |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50ecc91148190aa820fcd466009ce |
completed | April 19, 2026, 5:20 p.m. |
| PD | Predicate disambiguation | batch_69e44fe91bc08190906518e1b120fcf0 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:36 a.m.