Based on the feature description and task, it seems that we are analyzing the relationship between the "Repetition" feature and the target variable, which is the grain yield of a soybean cultivar (low or high).

To analyze this relationship, we need to have some prior knowledge or understanding of the feature "Repetition" and its possible ranges of values.

Assuming that the "Repetition" feature represents the number of times a specific cultivar was repeated or replicated in the experiment, we can infer that different repetitions may have an impact on the grain yield.

Based on this understanding, we can create a dictionary that includes five typical "Repetition" values for each target class ('low', 'high'). Here's the code snippet:

```json
{
	"low": [1.0, 2.0, 3.0, 4.0, 5.0], 
	"high": [10.0, 12.0, 15.0, 18.0, 20.0]
}
```

Please note that the specific values mentioned here are just examples and should be adjusted based on your prior knowledge or any specific information you have about the dataset.