Triple
T3507287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AlexNet |
E74105
|
entity |
| Predicate | inputImageSize |
P31979
|
FINISHED |
| Object | 224x224 pixels (approximately) |
—
|
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: 224x224 pixels (approximately) | Statement: [AlexNet, inputImageSize, 224x224 pixels (approximately)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inputImageSize Context triple: [AlexNet, inputImageSize, 224x224 pixels (approximately)]
-
A.
inputFrameSize
chosen
Indicates the size or dimensions of the input frame used or processed in a given context.
-
B.
modelSize
Indicates the quantitative measure of how large or complex a model is, typically in terms of parameters, layers, or memory footprint.
-
C.
romSize
Indicates the amount of read-only memory (ROM) associated with an entity, typically measured as its storage capacity.
-
D.
sensorResolution
Indicates the level of detail or precision with which a sensor can measure or distinguish changes in the observed quantity or environment.
-
E.
paletteSize
Indicates the number of distinct colors included in a given color palette.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc0b635c81909bc95ba2562d8f94 |
completed | March 8, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69adae0e770481908528fa35eda53003 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:18 p.m.