Triple
T32415323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anomaly Detector |
E828319
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | anomaly detection service |
C53496
|
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: anomaly detection service Context triple: [Anomaly Detector, instanceOf, anomaly detection service]
-
A.
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.
-
B.
complex event processing service
chosen
A complex event processing service continuously ingests and analyzes streams of events in real time to detect patterns, correlations, and anomalies and trigger appropriate actions or alerts.
-
C.
cloud-native observability service
A cloud-native observability service is a scalable, distributed platform that collects, correlates, and analyzes metrics, logs, and traces from cloud-native applications and infrastructure to provide real-time visibility, alerting, and insights into system health and performance.
-
D.
risk and compliance data service
A risk and compliance data service aggregates, analyzes, and delivers regulatory, risk, and compliance-related information to help organizations identify exposures, meet legal obligations, and support informed governance decisions.
-
E.
service supervision suite
A service supervision suite is a coordinated set of tools and processes that continuously monitor, manage, and automatically recover services to ensure reliability, performance, and minimal downtime.
- 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_69f34919f300819092b541c6277cd68a |
completed | April 30, 2026, 12:20 p.m. |
Created at: May 1, 2026, 12:54 a.m.