Based on prior knowledge, the feature "capital-gain" represents the amount of capital gains a person earned in a particular year. Capital gain is the profit from the sale of an investment, such as stocks, bonds, or real estate. To analyze the relationship between the feature and the target variable, we can consider the following:

1. People who earn more than $50,000 per year are likely to have higher capital gains compared to those who earn less. Therefore, we expect to see higher values of capital-gain for the target class "yes."

2. People who earn less than or equal to $50,000 per year may still have some capital gains, but they are likely to be lower compared to the target class "yes."

Based on these assumptions, we can create a dictionary to present the relationship between the feature "capital-gain" and the target variable "Does this person earn more than 50000 dollars per year?". Let's generate the dictionary:

```json
{
	"no": [0, 100, 500, 1000, 2000],
	"yes": [2000, 5000, 10000, 20000, 50000]
}
```

In the dictionary, the key "no" represents the target class "no" (people who earn less than or equal to $50,000 per year). The values in the corresponding list are possible values of the feature "capital-gain" for this target class. Similarly, the key "yes" represents the target class "yes" (people who earn more than $50,000 per year) with the corresponding list of possible feature values. The values in the lists are rounded to integers for simplicity.

Please note that the actual relationship between the feature and the target could be more complex, and this analysis is based on general assumptions. Further exploration with dataset-specific analysis and statistical methods would provide more accurate insights.