Triple
T26105243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rome |
E658518
|
entity |
| Predicate | maxCoreCountPerCPU |
P159947
|
FINISHED |
| Object | 64 |
—
|
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: 64 | Statement: [Rome, maxCoreCountPerCPU, 64]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxCoreCountPerCPU Context triple: [Rome, maxCoreCountPerCPU, 64]
-
A.
maxCoresPerCluster
Indicates the maximum number of processing cores that are allowed or allocated within a single cluster.
-
B.
maxNumberOfProcessors
Indicates the maximum number of processors that are allowed or supported in a given context or configuration.
-
C.
threadsPerCore
Indicates the number of hardware or logical execution threads that are assigned to run on each individual processor core.
-
D.
maxCoresPerChiplet
Indicates the maximum number of processing cores that are allowed or supported on a single chiplet.
-
E.
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.
- 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_69ee5bc09c288190bc42a11972841383 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f60775b298819095c64aca6806d61c |
completed | May 2, 2026, 2:17 p.m. |
| PD | Predicate disambiguation | batch_69f5b0021da88190bdd4cf2698c23edf |
completed | May 2, 2026, 8:04 a.m. |
| PDg | Predicate description generation | batch_69f5f6b32a8881909baa0db57b80d56a |
completed | May 2, 2026, 1:05 p.m. |
Created at: April 26, 2026, 7:58 p.m.