Triple
T17561523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dataflow worker |
E427704
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Google Cloud Dataflow component |
C26346
|
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: Google Cloud Dataflow component Context triple: [Dataflow worker, instanceOf, Google Cloud Dataflow component]
-
A.
data engineering tool
A data engineering tool is a software solution that enables the collection, transformation, orchestration, and management of data pipelines to ensure reliable, scalable, and efficient data processing.
-
B.
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.
-
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.
data processing platform
chosen
A data processing platform is an integrated system that ingests, transforms, analyzes, and manages data at scale to enable efficient, reliable, and repeatable data-driven operations and insights.
-
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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 10, 2026, 5:50 a.m.