The feature "capital-gain" represents the capital gain that a person had in the last year. To analyze its relationship with the target variable, we can consider that individuals who earn more than $50,000 per year may have higher capital gains compared to those who earn less.

Based on this assumption, we can divide the feature into two categories: capital gains for individuals who earn more than $50,000 per year (target class "yes") and capital gains for individuals who earn less or equal to $50,000 per year (target class "no").

Let's create a dictionary with the possible values of the feature "capital-gain" for each target class.

```json
{
	"no": [0, 500, 1000, 1500, 2000],
	"yes": [5000, 10000, 15000, 20000, 25000]
}
```

In this dictionary, for the target class "no", we have included the values 0, 500, 1000, 1500, and 2000 as representative examples of capital gains for individuals earning less or equal to $50,000 per year. Similarly, for the target class "yes", we have included the values 5000, 10000, 15000, 20000, and 25000 as representative examples of capital gains for individuals earning more than $50,000 per year. These values are just examples and more specific ranges can be defined based on the dataset and prior knowledge.