Triple
T32415418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azure Time Series Insights |
E828321
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | time series analytics service |
C58657
|
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: time series analytics service Context triple: [Azure Time Series Insights, instanceOf, time series analytics service]
-
A.
time series query language
A time series query language is a specialized language designed to efficiently retrieve, aggregate, and analyze data points indexed in time order from time series databases or systems.
-
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.
forecasting report series
A forecasting report series is a recurring set of structured documents that present projected future conditions, trends, or outcomes over time based on analyzed data and modeling.
-
D.
analytics pioneer
An analytics pioneer is a visionary leader who leverages emerging data techniques and technologies to uncover novel insights and shape new standards in data-driven decision-making.
-
E.
U.S. economic time series
A U.S. economic time series is a chronologically ordered sequence of quantitative observations that track the evolution of a specific economic indicator (such as GDP, inflation, or unemployment) in the United States over time.
- 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_69f34919f300819092b541c6277cd68a |
completed | April 30, 2026, 12:20 p.m. |
Created at: May 1, 2026, 12:54 a.m.