Triple
T32415321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anomaly Detector |
E828319
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Azure Cognitive Service |
C59513
|
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: Azure Cognitive Service Context triple: [Anomaly Detector, instanceOf, Azure Cognitive Service]
-
A.
Azure Cognitive Service
chosen
Azure Cognitive Service is a cloud-based collection of AI-powered APIs and tools that enable developers to easily add capabilities like vision, speech, language understanding, and decision-making to their applications without needing deep machine learning expertise.
-
B.
natural language understanding platform
A natural language understanding platform is a system that interprets, analyzes, and derives meaning from human language input to enable intelligent, context-aware interactions and automation.
-
C.
AI research tool
An AI research tool is a software system that leverages artificial intelligence techniques to assist in discovering, organizing, analyzing, and generating scientific knowledge and insights.
-
D.
speech recognition API
A speech recognition API is a software interface that converts spoken language into machine-readable text or commands, enabling applications to process and respond to voice input.
-
E.
generative AI service suite
A generative AI service suite is an integrated collection of tools and APIs that create, transform, and analyze content (such as text, images, code, or audio) using advanced machine learning models to support diverse applications and workflows.
- 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.