Triple
T18300614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray Serve |
E438347
|
entity |
| Predicate | domain |
P87
|
FINISHED |
| Object | MLOps |
—
|
NE NERFINISHED |
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: MLOps | Statement: [Ray Serve, domain, MLOps]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MLOps Context triple: [Ray Serve, domain, MLOps]
-
A.
Machine Learning Engineering for Production (MLOps)
chosen
Machine Learning Engineering for Production (MLOps) is a specialized online course that teaches how to design, deploy, and maintain scalable, reliable machine learning systems in real-world production environments.
-
B.
Kubeflow Pipelines
Kubeflow Pipelines is a platform for building, deploying, and managing end-to-end machine learning workflows on Kubernetes using containerized components.
-
C.
AutoML
AutoML is a set of machine learning tools and services that automatically build, train, and optimize models with minimal manual coding or expertise.
-
D.
Machine Learning Lab
Machine Learning Lab is a research center at the International Institute of Information Technology, Hyderabad, focused on advancing theory and applications of machine learning and artificial intelligence.
-
E.
Create ML
Create ML is Apple's machine learning tool that lets developers easily build and train models directly on macOS using simple, user-friendly interfaces.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5017f63dc819083a675d570620f2f |
completed | April 19, 2026, 4:23 p.m. |
Created at: April 10, 2026, 10:35 a.m.