Triple
T5454082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | "Not with a bang but a whimper" |
E122436
|
entity |
| Predicate | rhetoricalEffect |
P834
|
FINISHED |
| Object | irony |
—
|
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: irony | Statement: ["Not with a bang but a whimper", rhetoricalEffect, irony]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rhetoricalEffect Context triple: ["Not with a bang but a whimper", rhetoricalEffect, irony]
-
A.
rhetoricalDevice
chosen
Indicates that one entity is used as a rhetorical device in relation to another, such as a figure of speech, stylistic technique, or persuasive strategy within a discourse.
-
B.
rhetoricalStyle
Indicates the characteristic manner or technique of expression used in communication, such as tone, structure, and persuasive strategies.
-
C.
eventEffect
Indicates the resulting change, outcome, or consequence that one event has on another state, entity, or event.
-
D.
encodingImpact
Indicates how one encoding or encoding choice affects, modifies, or constrains another process, representation, or outcome.
-
E.
emotionEffect
Indicates that one entity’s emotional state causes or influences a change in another entity’s feelings, behavior, or condition.
- 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_69bd46424248819085282ddf50a565f3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd927c946c8190aef40679199fede3 |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a0d96c8190bd1299edbf764bbb |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:08 p.m.