Triple
T3937247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Ladies and Gentlemen |
E90941
|
entity |
| Predicate | literaryTone |
P16928
|
FINISHED |
| Object | witty |
—
|
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: witty | Statement: [Southern Ladies and Gentlemen, literaryTone, witty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryTone Context triple: [Southern Ladies and Gentlemen, literaryTone, witty]
-
A.
literaryLanguage
Indicates that an entity is expressed, written, or communicated using a particular literary or standardized written language.
-
B.
literaryFeature
chosen
Indicates a relationship where something possesses or exhibits a characteristic, device, or stylistic element used in literature.
-
C.
literarySubject
Indicates that one entity serves as the subject, topic, or focus of a literary work created by another entity.
-
D.
rhetoricalStyle
Indicates the characteristic manner or technique of expression used in communication, such as tone, structure, and persuasive strategies.
-
E.
inLiterature
Indicates that a work, concept, or entity is mentioned, discussed, or represented within a piece of literature.
- 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_69aed95f26e0819094b0e71974543a19 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeedceab608190934293d432f14476 |
completed | March 9, 2026, 3:57 p.m. |
| PD | Predicate disambiguation | batch_69aee7625ad4819097e4e8a168c19274 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:24 p.m.