Triple
T428741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rip Van Winkle |
E9666
|
entity |
| Predicate | hasLiteraryDevice |
P6480
|
FINISHED |
| Object | frame narrative |
—
|
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: frame narrative | Statement: [Rip Van Winkle, hasLiteraryDevice, frame narrative]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLiteraryDevice Context triple: [Rip Van Winkle, hasLiteraryDevice, frame narrative]
-
A.
rhetoricalDevice
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.
hasLiteraryForm
chosen
Indicates that one entity is expressed, structured, or realized in a particular literary form (such as a genre, style, or textual format).
-
C.
literaryLanguage
Indicates that an entity is expressed, written, or communicated using a particular literary or standardized written language.
-
D.
literarySource
Indicates that one entity serves as the written or literary origin, reference, or basis for another entity.
-
E.
hasLyricalStyle
Indicates that one entity possesses or is characterized by a particular lyrical style in relation to another entity or context.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eeecb64c81908c5c83ef7c0181e6 |
completed | Feb. 28, 2026, 1:34 p.m. |
| PD | Predicate disambiguation | batch_69a2edd7a3608190b8785c7b7205f6c1 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.