Based on prior knowledge, the relationship between the feature "Sex" and the task of determining whether the coronary angiography shows heart disease is not very strong. While certain heart diseases may have a higher prevalence in one sex compared to the other, overall, the correlation between sex and heart disease is not very pronounced.

However, we can still analyze the relationship between the feature "Sex" and the target variable "heart disease" by examining the available data. From the given feature description, the feature "Sex" is a categorical variable with categories "M" and "F", representing male and female patients, respectively.

To create the required dictionary, we need information about the possible values of the feature "Sex" for each target class ("no" and "yes"). However, since we don't have access to the actual data, we cannot determine the specific values without further information. 

Given this limitation, we can formulate a general dictionary based on common observations:

```json
{
	"no": ["M", "F"],
	"yes": ["M", "F"]
}
```

This dictionary assumes that both sexes ("M" and "F") are represented in both target classes ("no" and "yes"). However, this is  a general presumption, and without actual data, we cannot accurately determine all the possible values of the feature "Sex" for each target class specifically.