Triple
T19083501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suit of Cups |
E467085
|
entity |
| Predicate | hasTraditionalImagery |
P5310
|
FINISHED |
| Object | cups |
—
|
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: cups | Statement: [Suit of Cups, hasTraditionalImagery, cups]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraditionalImagery Context triple: [Suit of Cups, hasTraditionalImagery, cups]
-
A.
hasImageryFrom
Indicates that one entity contains, incorporates, or is derived from the imagery produced or provided by another entity.
-
B.
hasTraditionalColors
Indicates that an entity is associated with colors that are traditionally or customarily linked to it.
-
C.
hasColorImagery
Indicates that something includes or is characterized by visual elements emphasizing specific colors or color-based symbolism.
-
D.
hasIconographicTradition
chosen
Indicates that an entity is associated with a recognized, recurring set of visual motifs or symbolic representations within a particular artistic or cultural tradition.
-
E.
usesImageryOf
Indicates that one entity employs or incorporates visual or sensory imagery that depicts, references, or symbolically represents 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_69d8dd04f4488190b1121cc53ef2bfd6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e2eab58c8190a987c88d633c92ec |
completed | April 20, 2026, 8:25 a.m. |
| PD | Predicate disambiguation | batch_69e4b9a604308190a3235184f9f2c056 |
completed | April 19, 2026, 11:16 a.m. |
Created at: April 10, 2026, 12:04 p.m.