Triple
T27798566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DirectQuery |
E702180
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Microsoft analytics services feature |
C39308
|
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: Microsoft analytics services feature Context triple: [DirectQuery, instanceOf, Microsoft analytics services feature]
-
A.
Microsoft developer platform service
A Microsoft developer platform service is a cloud-based or on-premises offering that provides tools, runtimes, APIs, and infrastructure to help developers build, deploy, and manage applications within the Microsoft ecosystem.
-
B.
serverless analytics service
chosen
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.
-
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.
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.
-
E.
SharePoint Server 2016 feature pack
SharePoint Server 2016 Feature Pack is a cumulative set of updates and enhancements that extend the functionality, security, and hybrid capabilities of SharePoint Server 2016 beyond its original release.
- 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_69ef8408e0588190977cffa32dc33a29 |
completed | April 27, 2026, 3:43 p.m. |
Created at: April 27, 2026, 5:32 p.m.