Triple
T17498826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amazon Athena |
E426142
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | serverless analytics service |
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: serverless analytics service Context triple: [Amazon Athena, instanceOf, serverless analytics 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.
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.
-
C.
online analytical processing server
An online analytical processing server is a specialized system that stores, organizes, and processes multidimensional data to support fast, complex analytical queries and business intelligence reporting.
-
D.
serverless computing framework
A serverless computing framework is a platform that automatically manages infrastructure, scaling, and execution of code in response to events, allowing developers to deploy functions without provisioning or maintaining servers.
-
E.
in-memory analytics engine
An in-memory analytics engine is a software system that stores and processes data primarily in main memory to deliver extremely fast analytical queries and real-time insights.
- F. None of above. chosen
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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 10, 2026, 5:48 a.m.