Based on prior knowledge, the number of stems (NLP) of a soybean cultivar can potentially have an impact on its grain yield. However, without specific domain knowledge or data, it is difficult to determine the exact relationship between NLP and the grain yield of the soybean cultivar.

To create the dictionary with possible values of NLP for each target class ('low' and 'high'), we can assume hypothetical ranges of NLP values for the different target classes and provide examples. Please note that these values are just for illustration purposes and may not represent the actual relationship between NLP and grain yield:

```json
{
	"low": [2.1, 2.3, 2.5, 2.8, 3.0],
	"high": [3.5, 3.7, 3.9, 4.2, 4.4]
}
```

In the example above, the dictionary suggests that for the target class 'low' (indicating low grain yield), some possible values of NLP could be around 2.1, 2.3, 2.5, 2.8, and 3.0. On the other hand, for the target class 'high' (indicating high grain yield), possible values of NLP could be around 3.5, 3.7, 3.9, 4.2, and 4.4.

Keep in mind that without access to real data or a detailed understanding of the domain, these values are just representative examples. The actual relationship between NLP and grain yield would require more specific information.