Triple
T20349862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Game Boy |
E495978
|
entity |
| Predicate | colorPaletteCount |
P121142
|
FINISHED |
| Object | approximately 32 built-in palettes |
—
|
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: approximately 32 built-in palettes | Statement: [Super Game Boy, colorPaletteCount, approximately 32 built-in palettes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colorPaletteCount Context triple: [Super Game Boy, colorPaletteCount, approximately 32 built-in palettes]
-
A.
colorVarietyCount
Indicates the number of distinct colors associated with or present in a given entity or set of entities.
-
B.
hasNumberOfColors
chosen
Indicates the quantity of distinct colors associated with an entity.
-
C.
paletteSize
Indicates the number of distinct colors included in a given color palette.
-
D.
maxColorsOnScreen
Indicates the maximum number of distinct colors that can be displayed on the screen at the same time.
-
E.
colorVarietyOf
Indicates that one entity represents a specific color variant or color option of 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_69e0b4a3320881909495ae8bc30bc2dc |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6784eacf4819095504e541d1d284d |
completed | April 20, 2026, 7:02 p.m. |
| PD | Predicate disambiguation | batch_69e57636b4808190bc2855af48a3ccdc |
completed | April 20, 2026, 12:41 a.m. |
Created at: April 16, 2026, 11:24 a.m.