Triple
T18205339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AutoConfig |
E435887
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Hugging Face Transformers utility class |
C39890
|
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: Hugging Face Transformers utility class Context triple: [AutoConfig, instanceOf, Hugging Face Transformers utility class]
-
A.
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.
-
B.
multimodal large language model family
A multimodal large language model family is a group of related neural models that can jointly process and generate multiple data modalities—such as text, images, audio, or video—using shared architectures, training objectives, and parameterizations.
-
C.
large language model family
A large language model family is a group of related neural network models that share a common architecture and training paradigm but vary in size, capabilities, and specialization to handle diverse natural language understanding and generation tasks.
-
D.
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.
-
E.
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.
- 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
Created at: April 10, 2026, 10:32 a.m.