Triple
T20424284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Allegory of the Eucharist |
E500951
|
entity |
| Predicate | usesVisualContrast |
P76521
|
FINISHED |
| Object | light and shadow |
—
|
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: light and shadow | Statement: [The Allegory of the Eucharist, usesVisualContrast, light and shadow]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesVisualContrast Context triple: [The Allegory of the Eucharist, usesVisualContrast, light and shadow]
-
A.
providesContrastWith
Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
-
B.
achievesContrast
chosen
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
C.
contrastRatio
Indicates the proportional difference in luminance or intensity between two visual elements being compared.
-
D.
createsContrastIn
Indicates a relationship where one element is used to highlight or emphasize differences with another element within a given context.
-
E.
contrastUse
Indicates that one entity is used in opposition or distinction to another to highlight differences between them.
- 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_69e0b4aa68fc8190b1a14c55575ef04a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67ba7afa4819095c9b75fab3cbbfc |
completed | April 20, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69e5766df0008190a73c4f613c29678f |
completed | April 20, 2026, 12:42 a.m. |
Created at: April 16, 2026, 11:30 a.m.