Based on prior knowledge, we can analyze the relationship between the feature "DLIT_AG" and the task of determining chronic heart failure. 

To do this, we need to examine how the different categories of the "DLIT_AG" variable relate to the presence or absence of chronic heart failure.

Looking at the feature description, we can make some assumptions:

1. If the category is "there was no arterial hypertension," it is less likely to be associated with chronic heart failure.
2. Categories indicating longer duration of arterial hypertension, such as "more than 10 years", "6-10 years", or "five years", may have a higher likelihood of chronic heart failure.
3. Categories indicating shorter duration, such as "three years", "two years", "one year", or "four years", may have a lower likelihood of chronic heart failure.

Based on these assumptions, we can create a dictionary to summarize the relationship:

```json
{
	"no": ["there was no arterial hypertension", "three years", "two years", "one year", "four years"], 
	"yes": ["more than 10 years", "6-10 years", "five years"]
}
```

Please note that this analysis is based on assumptions and general knowledge. The actual relationship between the feature and the target variable should be validated using statistical analysis or machine learning techniques.