Based on my prior knowledge, let's analyze the relationship between the feature "capital-loss" and the task of whether a person earns more than $50,000 per year.

In general, a capital loss refers to the loss incurred when selling a capital asset, such as stocks or real estate, for a price lower than its original purchase price. However, it is important to note that the feature "capital-loss" in this context refers specifically to the capital loss experienced by an individual in the previous year.

Analyzing the relationship, we can assume that individuals who earned less than $50,000 per year are less likely to have significant capital losses. On the other hand, individuals who earned more than $50,000 per year might have a wider range of capital losses since they have more investments and assets.

Based on this analysis, the dictionary with possible values of the "capital-loss" feature for each target class can be constructed as follows:

```json
{
	"no": [-1000, -500, -200, -100, -50],
	"yes": [-5000, -10000, -20000, -30000, -40000]
}
```

Explanation:
For the target class "no" (earnings less than $50,000 per year), some possible values of "capital-loss" could be -1000, -500, -200, -100, and -50 (representing smaller losses or even gains).

For the target class "yes" (earnings more than $50,000 per year), some possible values of "capital-loss" could be -5000, -10000, -20000, -30000, and -40000 (representing larger losses due to higher investment activities).

It is important to note that these values are just examples and can be adjusted based on the specific dataset and domain knowledge.