Triple
T35914219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sieve Portrait |
E1038700
|
entity |
| Predicate | hasAllegoricalMotif |
P24039
|
FINISHED |
| Object | sieve |
—
|
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: sieve | Statement: [Sieve Portrait, hasAllegoricalMotif, sieve]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAllegoricalMotif Context triple: [Sieve Portrait, hasAllegoricalMotif, sieve]
-
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.
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).
-
D.
containsAllusion
Indicates that one entity includes or incorporates an indirect reference or allusion to another entity.
-
E.
allegoryType
Indicates that one entity serves as a specific kind or category of allegory in relation to another entity.
- 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_69f76e2320748190b7f5c4750d0cd0d3 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fddf721c1481909301a0f379368f10 |
completed | May 8, 2026, 1:04 p.m. |
| PD | Predicate disambiguation | batch_69fddda1ae7c8190b5848ff9a9e39826 |
completed | May 8, 2026, 12:57 p.m. |
Created at: May 3, 2026, 4:07 p.m.