Triple
T31709540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shawn Williams |
E809278
|
entity |
| Predicate | hasHumorFunction |
P172889
|
FINISHED |
| Object | reactive humor to Marlon’s antics |
—
|
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: reactive humor to Marlon’s antics | Statement: [Shawn Williams, hasHumorFunction, reactive humor to Marlon’s antics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHumorFunction Context triple: [Shawn Williams, hasHumorFunction, reactive humor to Marlon’s antics]
-
A.
hasHumorType
Indicates that an entity possesses or is characterized by a particular style, category, or type of humor.
-
B.
isHumorousWork
Indicates that a work is intended to be humorous or comedic in nature.
-
C.
hasHumorousSubplotActor
Indicates that an actor participates in or is responsible for a humorous subplot within a larger work.
-
D.
hasComedyElements
Indicates that something contains humorous or comedic aspects as part of its overall content or style.
-
E.
humorSetting
Indicates a relationship where one entity specifies or controls the level, style, or presence of humor applied to another entity or context.
- F. None of above. chosen
Provenance (4 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_69f348df4e048190a4a5a9932ada78d6 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6b0d21dd08190a9883ff71c94c71c |
completed | May 3, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69f6aca3dedc81908b519d53d2909868 |
completed | May 3, 2026, 2:02 a.m. |
| PDg | Predicate description generation | batch_69f6afeaaef88190aefa97e83f8db906 |
completed | May 3, 2026, 2:16 a.m. |
Created at: April 30, 2026, 11:15 p.m.