Triple
T7232979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | An die Freude |
E154945
|
entity |
| Predicate | literaryLanguageRegister |
P19921
|
FINISHED |
| Object | elevated poetic diction |
—
|
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: elevated poetic diction | Statement: [An die Freude, literaryLanguageRegister, elevated poetic diction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryLanguageRegister Context triple: [An die Freude, literaryLanguageRegister, elevated poetic diction]
-
A.
literaryLanguage
Indicates that an entity is expressed, written, or communicated using a particular literary or standardized written language.
-
B.
linguisticRegister
chosen
Indicates the level of formality or stylistic variety in which a linguistic expression is typically used within a given context.
-
C.
literarySubject
Indicates that one entity serves as the subject, topic, or focus of a literary work created by another entity.
-
D.
scriptureLanguageRegister
Indicates the specific linguistic register or style in which a piece of scripture is expressed (e.g., formal, liturgical, vernacular).
-
E.
literaryUniverse
Indicates that two or more works of literature exist within the same fictional universe or continuity, sharing settings, characters, or canonical events.
- 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_69c68811dd1c8190ac460bb39e64e1f0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea552a688190a00f5d0ad982f787 |
completed | March 27, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69c6e7644648819096a5e2de5d0dbe97 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:54 p.m.