Triple
T30872826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaggle National Data Science Bowl solution |
E786389
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Kaggle competition solution |
C57250
|
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: Kaggle competition solution Context triple: [Kaggle National Data Science Bowl solution, instanceOf, Kaggle competition solution]
-
A.
data visualization competition
A data visualization competition is an event where participants analyze data sets and create compelling visual representations to communicate insights, often judged on clarity, creativity, and analytical rigor.
-
B.
Tableau competition
A Tableau competition is an event where participants use Tableau software to analyze data and create visualizations, dashboards, and insights that are judged on accuracy, creativity, and storytelling.
-
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.
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.
-
E.
machine learning division
The machine learning division is an organizational unit responsible for researching, developing, and deploying data-driven algorithms and models to solve complex problems and enhance products or services.
- 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_69f224b9df2c819086f55f8bcf7f382e |
completed | April 29, 2026, 3:33 p.m. |
Created at: April 29, 2026, 8:48 p.m.