Triple
T18205282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VisionEncoderDecoderModel |
E435885
|
entity |
| Predicate | supportsMixedPrecision |
P130229
|
FINISHED |
| Object | True |
—
|
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: True | Statement: [VisionEncoderDecoderModel, supportsMixedPrecision, True]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsMixedPrecision Context triple: [VisionEncoderDecoderModel, supportsMixedPrecision, True]
-
A.
supportsQuantization
Indicates that one entity is capable of operating with, or is compatible with, quantized representations or computations of another entity.
-
B.
supportsFloatingPoint
Indicates that an entity is capable of handling or operating with floating-point (non-integer) numeric values.
-
C.
supportsGPUType
Indicates that one entity is compatible with, or capable of operating using, a specified type of GPU.
-
D.
gpuType
Indicates the specific kind or model category of GPU associated with an entity.
-
E.
supportsArbitraryPrecisionArithmetic
Indicates that the subject system or component can perform arithmetic operations with numbers of virtually unlimited size and precision, beyond fixed hardware-imposed limits.
- 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
| PDg | Predicate description generation | batch_69e438f684e48190b38c64b58c518b6a |
completed | April 19, 2026, 2:07 a.m. |
Created at: April 10, 2026, 10:32 a.m.