Based on my prior knowledge, the relationship between the Season feature and the grain yield of soybean cultivar can be analyzed as follows:

1. Low grain yield: Soybean cultivars with low grain yield are typically associated with Season values that fall within a certain range. These values could be below a certain threshold, depending on the specific conditions and criteria being considered. For example, if we assume that Season is measured on a scale of 1 to 2, a typical range for low grain yield could be Season values less than or equal to 1.2.

2. High grain yield: Soybean cultivars with high grain yield are generally associated with Season values that fall within a different range. These values could be above a certain threshold, again depending on the specific conditions and criteria. For example, if we assume that Season is measured on a scale of 1 to 2, a typical range for high grain yield could be Season values greater than 1.8.

Based on this analysis, the dictionary representing the relationship between the Season feature and the task can be constructed as follows:

```json
{
	"low": [1.0, 1.1, 1.2, 1.2, 1.2],
	"high": [1.9, 1.9, 2.0, 2.0, 2.0]
}
```

In this example, I have included 5 typical values for each target class, ['low', 'high'], based on the analysis mentioned above. These values are presented as floats in the lists for each target class.