Triple
T35959486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fujitsu A64FX |
E1039956
|
entity |
| Predicate | additionalCoreCount |
P109797
|
FINISHED |
| Object | 4 assistant cores |
—
|
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 assistant cores | Statement: [Fujitsu A64FX, additionalCoreCount, 4 assistant cores]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: additionalCoreCount Context triple: [Fujitsu A64FX, additionalCoreCount, 4 assistant cores]
-
A.
additionalAssistantCoresPerCPU
chosen
Indicates the number of extra assistant processing cores that are allocated for each CPU unit.
-
B.
bigCoreCount
Indicates that an entity (such as a processor or system) has a relatively large number of cores compared to a typical or baseline configuration.
-
C.
smallCoreCount
Indicates that an entity has a relatively low number of processing cores compared to typical or expected configurations.
-
D.
efficiencyCores
Indicates that the related cores are optimized for energy-efficient, low-power processing rather than maximum performance.
-
E.
gpuCoreCount
Indicates the number of processing cores present in a GPU.
- 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_69f76e26b21081909fd9ffb3aff6c77a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7ac23d1388190bdf9628b294943bd |
completed | May 3, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69f7ab734d848190a84f9b8c3a952b75 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 3, 2026, 4:07 p.m.