Triple
T19749952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AnalyticDB |
E474349
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | cloud-native data warehouse service |
C25904
|
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: cloud-native data warehouse service Context triple: [AnalyticDB, instanceOf, cloud-native data warehouse service]
-
A.
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.
-
B.
data warehouse platform
chosen
A data warehouse platform is an integrated system that collects, stores, and organizes large volumes of structured data from multiple sources to support efficient querying, reporting, and analytics for business decision-making.
-
C.
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.
-
D.
database service provider
A database service provider is an entity that offers infrastructure, tools, and managed services for storing, organizing, securing, and accessing data in databases for clients or applications.
-
E.
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.
- 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_69d8e51940a0819087bd2996f98da668 |
completed | April 10, 2026, 11:55 a.m. |
Created at: April 10, 2026, 1:47 p.m.