Triple
T27665037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Google Natural Language API |
E697206
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | cloud-based natural language processing service |
C23211
|
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 natural language processing service Context triple: [Google Natural Language API, instanceOf, cloud-based natural language processing service]
-
A.
natural language understanding platform
chosen
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.
-
B.
natural language processing model
A natural language processing model is a computational system designed to understand, interpret, generate, and manipulate human language in a meaningful way.
-
C.
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.
-
D.
natural language processing technique
A natural language processing technique is a computational method or algorithm designed to enable computers to understand, interpret, generate, or manipulate human language in a meaningful way.
-
E.
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.
- 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_69ef590b85a4819083ec7c12bd3c9c10 |
completed | April 27, 2026, 12:39 p.m. |
Created at: April 27, 2026, 2:37 p.m.