Triple
T1636003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unified Modeling Language |
E35361
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | visual modeling language |
C11235
|
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: visual modeling language Context triple: [Unified Modeling Language, instanceOf, visual modeling language]
-
A.
data visualization platform
A data visualization platform is a software system that enables users to transform raw data into interactive, graphical representations to explore insights, identify patterns, and communicate information effectively.
-
B.
vector graphics editor
A vector graphics editor is a software application used to create and manipulate images composed of scalable geometric shapes such as lines, curves, and polygons, allowing for resolution-independent artwork and precise design control.
-
C.
Knowledge representation language
A knowledge representation language is a formal system used to encode information about the world in a structured, machine-interpretable way so that computers can reason about it.
-
D.
user interface design language
A user interface design language is a standardized set of visual, interaction, and behavioral guidelines that define how digital interfaces should look and function to ensure consistency and usability across products.
-
E.
Web ontology language
A web ontology language is a formal language designed for representing rich, machine-interpretable knowledge about concepts, relationships, and constraints on the web to enable automated reasoning and interoperability.
- 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_69a886036bc081909ff5de16dbe5e8ea |
completed | March 4, 2026, 7:20 p.m. |
Created at: March 4, 2026, 7:28 p.m.