Triple
T672122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | All for one and one for all |
E12993
|
entity |
| Predicate | hasRhetoricalDevice |
P834
|
FINISHED |
| Object | parallelism |
—
|
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: parallelism | Statement: [All for one and one for all, hasRhetoricalDevice, parallelism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRhetoricalDevice Context triple: [All for one and one for all, hasRhetoricalDevice, parallelism]
-
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.
hasAnaphora
Indicates that one expression in a text refers back to another earlier expression for its interpretation.
-
C.
hasMetafictionalRole
Indicates that an entity plays a role within a story that self-consciously comments on, references, or breaks the conventions of fiction itself.
-
D.
hasConnotation
Indicates that one entity carries an implied or associated meaning, tone, or emotional nuance in relation to another entity.
-
E.
hasLinguisticFeature
Indicates that an entity possesses a particular linguistic property, trait, or characteristic.
- 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a1b3682c8190a9b9a454480c3446 |
completed | March 1, 2026, 8:29 p.m. |
| PD | Predicate disambiguation | batch_69a49d1a16c48190af89e3b078a4957e |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.