Triple
T38309338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AMD Instinct MI100 |
E1033641
|
entity |
| Predicate | peakFP16Performance |
P88583
|
FINISHED |
| Object | 46.1 TFLOPS |
—
|
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: 46.1 TFLOPS | Statement: [AMD Instinct MI100, peakFP16Performance, 46.1 TFLOPS]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakFP16Performance Context triple: [AMD Instinct MI100, peakFP16Performance, 46.1 TFLOPS]
-
A.
floatingPointPerformance
chosen
Indicates the level of computational capability or efficiency an entity has when performing floating-point arithmetic operations.
-
B.
bf16MatrixPeak
Indicates that a given element in a bfloat16 matrix is a local maximum relative to its neighboring elements under a specified comparison criterion.
-
C.
tensorCores
Indicates that the relationship or operation involves the use of specialized tensor processing cores (e.g., hardware units optimized for tensor or matrix computations).
-
D.
floatingPointPipelines
Indicates a relationship where one or more entities are involved in, implement, or are associated with pipelines that process floating-point numerical operations.
-
E.
neuralEnginePerformance
Indicates the level or efficiency of processing capability provided by a neural engine in performing AI or machine-learning tasks.
- 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_69f76e132c408190969b3d35c04b87ae |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fcd313e61c8190b174b331365b803f |
completed | May 7, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f6b2e08190bf0300ae7c9ae67a |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 3, 2026, 4:30 p.m.