It depends on the signal-to-noise in the dataset. The amount of data to perform named entity disambiguation will depend on the tf-idf score of the occupation and skills, rare occupations and skills will require less data to build a performant model.
For example, that the sentence "I am a cook that multitasks well." "Cook" is an occupation and "multitask" is a relevant skill. In a similar sentence, "I multitasked while I cooked." "Cook" is no longer an occupation and "multitask" is no longer a relevant skill. However, the phrase "saturation diver" is less frequent than "cook", thus much easier to build a model to identify as an occupation and find relevant skills.
Annotator performance is easier to measure. Cohen's kappa is a common method of judging inter-rater reliability. Again, the number of needed raters depends on their agreement on the task. If task performance is easy, the number of raters and the number of items per rater can be lower. It is best to benchmark your system and then decide how much data you need to raise the benchmark scores.
One way to automatically create ontologies from a text is the TextRank algorithm.