Triple
T27888032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azure HDInsight |
E705281
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | big data analytics service |
C26346
|
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: big data analytics service Context triple: [Azure HDInsight, instanceOf, big data analytics service]
-
A.
serverless analytics service
A serverless analytics service is a cloud-based platform that automatically provisions and scales compute resources to process and analyze data on demand, charging only for actual usage without requiring infrastructure management.
-
B.
collaborative data science platform
A collaborative data science platform is an integrated environment where multiple users can jointly develop, run, and share data workflows, analyses, and models using shared datasets, tools, and computational resources.
-
C.
data lake service
A data lake service is a scalable, centralized repository that stores vast amounts of raw, structured, and unstructured data and provides tools for ingestion, management, and analytics.
-
D.
managed data ingestion service
A managed data ingestion service is a fully hosted platform that reliably collects, transforms, and routes data from diverse sources into target systems at scale, handling infrastructure, scaling, and monitoring automatically.
-
E.
data processing platform
chosen
A data processing platform is an integrated system that ingests, transforms, analyzes, and manages data at scale to enable efficient, reliable, and repeatable data-driven operations and insights.
- 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_69ef96b39c448190a9b3aa6672a5168f |
completed | April 27, 2026, 5:02 p.m. |
Created at: April 27, 2026, 6:34 p.m.