Triple
T24763500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cosy Moments |
E619515
|
entity |
| Predicate | usedForSatireOf |
P114828
|
FINISHED |
| Object | journalism |
—
|
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: journalism | Statement: [Cosy Moments, usedForSatireOf, journalism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedForSatireOf Context triple: [Cosy Moments, usedForSatireOf, journalism]
-
A.
usedForHumor
chosen
Indicates that something is employed with the intention of being funny, amusing, or comical.
-
B.
hasSatiricalTone
Indicates that something expresses its content in a mocking, ironic, or humorous way to criticize or ridicule its subject.
-
C.
hasNotableSatire
Indicates that one entity is recognized for containing or exemplifying a significant satirical treatment of the other entity.
-
D.
hasHumorousTreatmentOf
Indicates that one entity presents or portrays another entity in a humorous, comedic, or joking manner.
-
E.
parodies
Indicates that one entity imitates another in an exaggerated or humorous way, often to criticize or comment on the original.
- 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_69e2fabbea94819092ed41348909622f |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f410a330f0819081bc60b9275d883d |
completed | May 1, 2026, 2:32 a.m. |
| PD | Predicate disambiguation | batch_69f40ef612c88190ab2f3f08d4a92018 |
completed | May 1, 2026, 2:24 a.m. |
Created at: April 18, 2026, 4:28 a.m.