Triple
T17498938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amazon Kinesis Data Analytics |
E426144
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | real-time analytics service |
C24092
|
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: real-time analytics service Context triple: [Amazon Kinesis Data Analytics, instanceOf, real-time analytics service]
-
A.
analytics platform
An analytics platform is a software system that collects, processes, and visualizes data from various sources to provide insights and support data-driven decision-making.
-
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.
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.
-
D.
in-memory analytics engine
chosen
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.
-
E.
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.
- 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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 10, 2026, 5:48 a.m.