Triple
T7935754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monochrome Display Adapter |
E184284
|
entity |
| Predicate | videoMemoryType |
P9896
|
FINISHED |
| Object | dedicated text buffer |
—
|
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: dedicated text buffer | Statement: [Monochrome Display Adapter, videoMemoryType, dedicated text buffer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: videoMemoryType Context triple: [Monochrome Display Adapter, videoMemoryType, dedicated text buffer]
-
A.
videoMemorySize
Indicates the amount of video memory associated with a graphics-related component or device.
-
B.
memoryType
chosen
Indicates the specific category or kind of memory associated with an entity or process.
-
C.
primaryMemoryType
Indicates the main or dominant type of memory associated with or used by an entity in a given context.
-
D.
gpuType
Indicates the specific kind or model category of GPU associated with an entity.
-
E.
maxUnifiedMemory
Indicates the maximum amount of unified (shared CPU/GPU) memory that can be allocated or used in a given context.
- 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_69ca8290c21c8190906a5ca6fe2b03c4 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3aec394081909a9569c02ac372af |
completed | March 31, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69cae9335f288190ba96781fd6576a2b |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:08 p.m.