Triple
T19434324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oracle Data Flow |
E486192
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | managed Apache Spark 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: managed Apache Spark service Context triple: [Oracle Data Flow, instanceOf, managed Apache Spark 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.
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.
-
D.
Snowflake compute resource
A Snowflake compute resource is a virtual warehouse of scalable compute clusters that execute queries and data processing tasks independently of storage within the Snowflake data platform.
-
E.
managed database service
A managed database service is a cloud-based offering where the provider handles database setup, maintenance, scaling, backups, and security, allowing users to focus on using the data rather than managing the infrastructure.
- 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
Created at: April 10, 2026, 1:37 p.m.