Triple
T10033333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IBM PowerHA for high availability |
E204902
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | clustering solution |
C27322
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: clustering solution Context triple: [IBM PowerHA for high availability, instanceOf, clustering solution]
-
A.
partition-based clustering method
A partition-based clustering method is an approach that divides a dataset into a predefined number of non-overlapping groups (clusters) by directly assigning each data point to exactly one cluster based on a chosen similarity or distance measure.
-
B.
high-availability solution
A high-availability solution is an architecture and set of mechanisms designed to ensure that a system or service remains continuously operational and accessible with minimal downtime, even in the face of failures or maintenance activities.
-
C.
dust solution
A dust solution is a cosmological model in general relativity describing a pressureless fluid (dust) whose gravitational field determines the spacetime geometry.
-
D.
high availability solution
A high availability solution is a system design and set of mechanisms that ensure critical services remain continuously accessible with minimal downtime, even in the face of failures or maintenance activities.
-
E.
unsupervised learning method
An unsupervised learning method is a type of machine learning approach that discovers patterns, structures, or groupings in unlabeled data without predefined output targets.
- F. None of above. chosen
Provenance (1 batch)
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_69ca834d77188190ad645e33e8ca3200 |
completed | March 30, 2026, 2:06 p.m. |
Created at: March 30, 2026, 8:54 p.m.