Named-entity recognition (NER) is typically solved as a sequence tagging task, i.e. the model is trained to predict a label for every word. Typically one annotates NER datasets using the IOB annotation format 1 (or one of its variants, like BIOES). Let’s take the example sentence from your paragraph. It would have to be annotated as follows:
the O
defaulting O
party O
shall O
have O
thirty B-TER
(30) I-TER
days I-TER
to O
cure O 
the O
default O
. O

In other words, we annotate each word as being either outside a named entity (“O”), inside a named-entity (“I-TER”) or at the beginning of a named entity (“B-TER”).
However, there’s one additional challenge, in the sense that models like BERT operate on subword tokens, rather than words, meaning that a word like “hello” might be tokenized into [“hel”, “lo”]. This means that one should actually labels all tokens rather than all words, as BERT will be trained to predict a label for every token. There are multiple strategies here, one could either propagate the label to all subtokens of a word, or only label the first subword token of a given word.
You can take a look at my example notebooks 6 that illustrate how to fine-tune BERT for NER.