Triple
T13789990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Made in America |
E331367
|
entity |
| Predicate | featuresSpokenHumor |
P45834
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Made in America, featuresSpokenHumor, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresSpokenHumor Context triple: [Made in America, featuresSpokenHumor, true]
-
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.
humorSource
Indicates that one entity is the origin or cause of humor experienced in relation to another entity.
-
D.
speakerFeatures
Indicates that certain characteristics, attributes, or properties are associated with a speaker in a given context.
-
E.
hasComedyElements
chosen
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_69d81c58feb08190a77bca8bf7d6d20f |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de024af32c8190a9bd1278e09564ba |
completed | April 14, 2026, 9 a.m. |
| PD | Predicate disambiguation | batch_69dbc85fb600819098a2aab48169be96 |
completed | April 12, 2026, 4:29 p.m. |
Created at: April 9, 2026, 10:11 p.m.