Triple
T36419989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rubin Science Platform |
E897124
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | cloud-based data analysis environment |
C33932
|
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-based data analysis environment Context triple: [Rubin Science Platform, instanceOf, cloud-based data analysis environment]
-
A.
collaborative data science platform
chosen
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.
-
B.
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.
-
C.
cloud computing platform
A cloud computing platform is an integrated environment that provides on-demand access to scalable computing resources, storage, and services over the internet, enabling users to deploy, manage, and run applications without managing underlying hardware.
-
D.
data processing platform
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.
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.
- 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_69f76e559b10819099d6655a6e14587c |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:10 p.m.