Triple
T8879930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abstract Wikipedia |
E211383
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | multilingual content generation system |
C20587
|
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: multilingual content generation system Context triple: [Abstract Wikipedia, instanceOf, multilingual content generation system]
-
A.
multilingual database
A multilingual database is a data storage system designed to store, manage, and retrieve information in multiple languages while preserving linguistic accuracy and supporting language-specific querying and processing.
-
B.
polyglot
A polyglot is a person who can understand and communicate in multiple languages with varying degrees of fluency.
-
C.
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.
-
D.
multilingual inscription
A multilingual inscription is a written text or engraving that presents the same or related content in two or more languages, often to communicate across linguistic groups or preserve information for diverse audiences.
-
E.
generative AI service suite
chosen
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_69ca838f9e20819096ab1f236a70381a |
completed | March 30, 2026, 2:07 p.m. |
Created at: March 30, 2026, 6:52 p.m.