Triple
T25607441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iron Viz competition |
E641951
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Tableau competition |
C50525
|
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: Tableau competition Context triple: [Iron Viz competition, instanceOf, Tableau competition]
-
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.
collaborative data science platform
A collaborative data science platform is an integrated environment where multiple users can jointly develop, run, and share data workflows, analyses, and models using shared datasets, tools, and computational resources.
-
C.
artificial intelligence competition
An artificial intelligence competition is an organized event where participants develop and pit AI systems against each other to solve defined tasks or challenges under specified rules and evaluation criteria.
-
D.
data visualization support service
A data visualization support service helps users design, build, and refine clear, effective visual representations of their data, providing tools, guidance, and technical assistance throughout the visualization process.
-
E.
business case competition
A business case competition is an event where teams analyze a real or simulated business problem and present strategic solutions to judges, competing on the quality, feasibility, and creativity of their recommendations.
- 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_69e75dc6ccf081908d49578fd36a76d5 |
completed | April 21, 2026, 11:21 a.m. |
Created at: April 21, 2026, 4:39 p.m.