Triple
T2312785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palatine lion |
E50995
|
entity |
| Predicate | colorContrast |
P21343
|
FINISHED |
| Object | black lion on a golden field |
—
|
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: black lion on a golden field | Statement: [Palatine lion, colorContrast, black lion on a golden field]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colorContrast Context triple: [Palatine lion, colorContrast, black lion on a golden field]
-
A.
themeContrast
Indicates a relationship where two themes are compared or opposed to highlight their differences or tension.
-
B.
hasMainContrast
chosen
Indicates a primary opposing or differing relationship between two elements, highlighting the main point of contrast between them.
-
C.
colorTheory
Indicates a relationship where principles or concepts about how colors interact, combine, or affect perception are applied or referenced between entities.
-
D.
usesColorDifferenceSignals
Indicates that one entity employs differences in color as signals to convey information or communicate.
-
E.
isShadeOf
Indicates that one entity represents a specific tonal or color variation derived from or closely related 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc685f05481909c863b29d1f6bacd |
completed | March 7, 2026, 6:32 a.m. |
| PD | Predicate disambiguation | batch_69abc58e88e481908733fdf79d3f8a15 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:49 p.m.