Based on our prior knowledge, we can analyze the relationship between the "educational-num" feature and the task of whether a person earns more than 50000 dollars per year.

The "educational-num" feature represents the highest level of education achieved in numerical form. Generally, higher levels of education are associated with higher income levels. Therefore, it is likely that individuals with higher values of "educational-num" will have a higher likelihood of earning more than 50000 dollars per year.

However, there can be cases where individuals with lower values of "educational-num" may still earn more than 50000 dollars per year due to factors such as experience, skills, or occupation.

Now, let's generate a dictionary with specific details:

```json
{
	"no": [1, 2, 3, 4, 5],
	"yes": [9, 10, 11, 12, 13]
}
```

In the dictionary, the "no" key represents the target class 'no', indicating individuals who do not earn more than 50000 dollars per year. The values in the corresponding list [1, 2, 3, 4, 5] represent possible values of the "educational-num" feature for this target class.

The "yes" key represents the target class 'yes', indicating individuals who earn more than 50000 dollars per year. The values in the corresponding list [9, 10, 11, 12, 13] represent possible values of the "educational-num" feature for this target class.

Please note that these values are provided as an example and may vary depending on the specific dataset being analyzed.