Triple
T22037968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FUN! |
E544258
|
entity |
| Predicate | containsIrony |
P71901
|
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: [FUN!, containsIrony, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsIrony Context triple: [FUN!, containsIrony, true]
-
A.
dramaticIrony
Indicates a situation where the audience or reader knows critical information that one or more characters do not, creating a contrast between character perception and reality.
-
B.
hasIronicMeaning
chosen
Indicates that something conveys a meaning opposite to or incongruent with its literal expression, creating an ironic effect.
-
C.
containsPoignancy
Indicates that something includes or conveys a strong sense of emotional depth, sadness, or touching significance.
-
D.
usesDoubleEntendre
Indicates that one entity employs language or expressions with a double meaning, often to convey a hidden or suggestive message alongside a literal one.
-
E.
containsAllusion
Indicates that one entity includes or incorporates an indirect reference or allusion 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_69e11e2f98c8819083e11eab90942a78 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127f32edc81909b6898af6621f56f |
completed | April 28, 2026, 9:34 p.m. |
| PD | Predicate disambiguation | batch_69e6f63b0d048190b241622759aab9de |
completed | April 21, 2026, 3:59 a.m. |
Created at: April 16, 2026, 8:25 p.m.