Triple
T30872827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaggle National Data Science Bowl solution |
E786389
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | image classification system |
C4178
|
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: image classification system Context triple: [Kaggle National Data Science Bowl solution, instanceOf, image classification system]
-
A.
image recognition model
chosen
An image recognition model is a computational system that analyzes visual input to automatically identify, classify, and sometimes localize objects, patterns, or features within images.
-
B.
statistical classification system
A statistical classification system is a structured framework that organizes data, entities, or phenomena into predefined categories based on quantitative criteria and statistical methods to enable consistent analysis and comparison.
-
C.
classification board
A classification board is an authoritative body or panel that evaluates and assigns categories, ratings, or classifications to items such as media, products, or information based on defined criteria and standards.
-
D.
plant classification system
A plant classification system is an organized framework that categorizes plants into hierarchical groups based on shared characteristics such as morphology, genetics, and evolutionary relationships.
-
E.
textual classification system
A textual classification system is a software component that automatically assigns predefined categories or labels to text inputs based on their content using rule-based, statistical, or machine learning methods.
- 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_69f224b9df2c819086f55f8bcf7f382e |
completed | April 29, 2026, 3:33 p.m. |
Created at: April 29, 2026, 8:48 p.m.