Triple
T1635699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MDA |
E35355
|
entity |
| Predicate | videoMemorySize |
P30786
|
FINISHED |
| Object | 4 KB |
—
|
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: 4 KB | Statement: [MDA, videoMemorySize, 4 KB]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: videoMemorySize Context triple: [MDA, videoMemorySize, 4 KB]
-
A.
modelSize
Indicates the quantitative measure of how large or complex a model is, typically in terms of parameters, layers, or memory footprint.
-
B.
memoryBandwidth
Indicates the rate at which data can be transferred to or from a memory system over a given period of time.
-
C.
displayResolution
Indicates the relationship specifying the width and height dimensions at which visual content is rendered or shown on a display.
-
D.
minRAM
Indicates that an entity requires at least a specified minimum amount of RAM to function or be considered valid.
-
E.
hasRAM
Indicates that an entity possesses or is equipped with a specified amount or type of random-access memory (RAM).
- F. None of above. chosen
Provenance (4 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_69a886036bc081909ff5de16dbe5e8ea |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a96083e7308190abbf025fe8e43abb |
completed | March 5, 2026, 10:52 a.m. |
| PD | Predicate disambiguation | batch_69a907cac610819083cafd4396b6d66c |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a9607716b4819092187f8c08daaf31 |
completed | March 5, 2026, 10:52 a.m. |
Created at: March 4, 2026, 7:28 p.m.