Triple
T29328480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microsoft Azure Text to Speech |
E743717
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Microsoft Azure Cognitive Service |
C29596
|
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: Microsoft Azure Cognitive Service Context triple: [Microsoft Azure Text to Speech, instanceOf, Microsoft Azure Cognitive Service]
-
A.
Microsoft developer platform service
chosen
A Microsoft developer platform service is a cloud-based or on-premises offering that provides tools, runtimes, APIs, and infrastructure to help developers build, deploy, and manage applications within the Microsoft ecosystem.
-
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.
OpenAI product
An OpenAI product is a software or service offering that applies OpenAI’s artificial intelligence models and technologies to solve specific user or business problems in a reliable, scalable, and user-friendly way.
-
D.
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.
-
E.
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.
- 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_69f09125f784819080f4e9fce9fe624f |
completed | April 28, 2026, 10:51 a.m. |
Created at: April 28, 2026, 1:28 p.m.