Triple
T7197453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gracemont |
E168650
|
entity |
| Predicate | maxCoresPerCluster |
P75686
|
FINISHED |
| Object | 4 |
—
|
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 | Statement: [Gracemont, maxCoresPerCluster, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxCoresPerCluster Context triple: [Gracemont, maxCoresPerCluster, 4]
-
A.
efficiencyCores
Indicates that the related cores are optimized for energy-efficient, low-power processing rather than maximum performance.
-
B.
maxHostsPerIMP
Indicates the maximum number of hosts that can be associated with or managed by a single IMP (Interface Message Processor).
-
C.
numberOfCoreEngines
Indicates the quantity of primary engines associated with an entity.
-
D.
maxSMPConfiguration
Indicates the configuration in which a system or component is set to use the maximum supported symmetric multiprocessing (SMP) resources or capabilities.
-
E.
maximumNumberOfSegments
Indicates the greatest allowable or observed count of discrete segments into which something can be or is divided.
- 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_69c68a5376748190bb500f03df86e93e |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6e92a5d288190955f703470e75bf3 |
completed | March 27, 2026, 8:31 p.m. |
| PD | Predicate disambiguation | batch_69c6e752385c819096fbab55566ee2a8 |
completed | March 27, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69c6e8b5f6508190af28e06a7959d717 |
completed | March 27, 2026, 8:29 p.m. |
Created at: March 27, 2026, 2:51 p.m.