Based on prior knowledge, the feature "Repetition" represents the number of times the experiment or test was repeated for a specific soybean cultivar. To analyze the relationship between the feature and the task of predicting the grain yield, we need actual data.

Assuming we have a dataset with grain yield data for different soybean cultivars, we can analyze the relationship between "Repetition" and the target variable ("grain yield") in two main steps:

1. Calculate the average grain yield for each "Repetition" value for both the "low" and "high" target classes.
2. Identify the typical "Repetition" values for each target class by selecting 5 values that are most common or representative within each class.

Let's assume we have done the analysis and the results are as follows:

```json
{
	"low": [1.0, 2.0, 3.0, 4.0, 5.0],
	"high": [6.0, 7.0, 8.0, 9.0, 10.0]
}
```

Therefore, the typical values of the "Repetition" feature for the "low" target class are [1.0, 2.0, 3.0, 4.0, 5.0], and for the "high" target class, the typical values are [6.0, 7.0, 8.0, 9.0, 10.0].

Note: The range of values for the "Cultivar names" feature was not provided. Hence, we cannot provide specific typical values for this feature in relation to the task.