Triple
T25932447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zen 2 |
E653468
|
entity |
| Predicate | maxCoresPerChiplet |
P159576
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Zen 2, maxCoresPerChiplet, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxCoresPerChiplet Context triple: [Zen 2, maxCoresPerChiplet, 8]
-
A.
maxCoresPerCluster
Indicates the maximum number of processing cores that are allowed or allocated within a single cluster.
-
B.
smallCoreCount
Indicates that an entity has a relatively low number of processing cores compared to typical or expected configurations.
-
C.
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.
-
D.
threadsPerCore
Indicates the number of hardware or logical execution threads that are assigned to run on each individual processor core.
-
E.
efficiencyCores
Indicates that the related cores are optimized for energy-efficient, low-power processing rather than maximum performance.
- 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_69e7ab3eb9b881909c1390690551f868 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f60419ef7881909fe061a3d1aa1ddf |
completed | May 2, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69f4a10480748190a2e67bd399fc435d |
completed | May 1, 2026, 12:48 p.m. |
| PDg | Predicate description generation | batch_69f55e497fa081909bc59a7b92c5df59 |
completed | May 2, 2026, 2:15 a.m. |
Created at: April 22, 2026, 8:37 a.m.