Triple
T25832099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neptunbrunnen |
E650690
|
entity |
| Predicate | hasAllegoricalFigure |
P24039
|
FINISHED |
| Object | River Rhine |
—
|
NE NERFINISHED |
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: River Rhine | Statement: [Neptunbrunnen, hasAllegoricalFigure, River Rhine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAllegoricalFigure Context triple: [Neptunbrunnen, hasAllegoricalFigure, River Rhine]
-
A.
hasAllegoricalFigures
chosen
Indicates that a work, scene, or element includes figures that symbolically represent abstract ideas, concepts, or moral qualities.
-
B.
hasAllegoricalDepictionsBy
Indicates that one entity is represented through allegorical depictions created by another entity.
-
C.
hasIconographicFigure
Indicates that one entity includes, depicts, or is associated with a particular iconographic figure in its visual or symbolic representation.
-
D.
hasMythicalFigure
Indicates that one entity is associated with, features, or includes a particular mythical or legendary figure.
-
E.
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).
- 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_69e7ab37438081908f1ccf6284839520 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f638d11c988190af7fd4572b08e038 |
completed | May 2, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69f63706b6008190993577193c85ff50 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 22, 2026, 7:39 a.m.