Triple
T18705451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kubeflow Pipelines |
E457355
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Kubeflow component |
C15636
|
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: Kubeflow component Context triple: [Kubeflow Pipelines, instanceOf, Kubeflow component]
-
A.
machine learning platform component
chosen
A machine learning platform component is a modular software element that provides specific functionality—such as data processing, model training, deployment, or monitoring—within an integrated ML lifecycle system.
-
B.
Kubernetes control plane component
A Kubernetes control plane component is a core service (such as the API server, scheduler, or controller manager) that collectively manages cluster state, scheduling, and orchestration of workloads.
-
C.
control plane component
A control plane component is a system element responsible for managing, configuring, and orchestrating the behavior and state of underlying data plane resources within a distributed or networked environment.
-
D.
machine learning model repository
A machine learning model repository is a centralized system for storing, versioning, organizing, and sharing trained models and their associated metadata throughout their lifecycle.
-
E.
cloud native project
A cloud native project is an application or system designed, built, and operated to fully leverage cloud computing models—such as containerization, microservices, dynamic orchestration, and managed services—for scalability, resilience, and rapid delivery.
- F. None of above.
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_69d8d392aad081909fe31aa03e6e97d1 |
completed | April 10, 2026, 10:40 a.m. |
Created at: April 10, 2026, 11:49 a.m.