Triple
T16759734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lover’s Confession |
E407307
|
entity |
| Predicate | GeniusAllegoryOf |
P44697
|
FINISHED |
| Object | reason |
—
|
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: reason | Statement: [The Lover’s Confession, GeniusAllegoryOf, reason]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: GeniusAllegoryOf Context triple: [The Lover’s Confession, GeniusAllegoryOf, reason]
-
A.
allegoricalInterpretation
Indicates that one entity is interpreted as symbolically representing deeper, often moral or spiritual, meanings within another entity (such as a text, image, or event).
-
B.
hasAllegoricalDepictionsBy
Indicates that one entity is represented through allegorical depictions created by another entity.
-
C.
allegoricalDomain
chosen
Indicates that one entity serves as the abstract or symbolic domain that another entity allegorically represents or refers to.
-
D.
hasAllegoricalFigures
Indicates that a work, scene, or element includes figures that symbolically represent abstract ideas, concepts, or moral qualities.
-
E.
politicalAllegoryFor
Indicates that one entity symbolically represents or critiques another entity, event, or system within a political 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_69d8839174188190909f190097207065 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3abeb3ab08190918f6bff686858be |
completed | April 18, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69e319cbd79c8190a03587a61c18bec0 |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:21 a.m.