What you need to look for is called "Named Entity recognition". From Wikipedia
Named-entity recognition (NER) (also known as entity identification,
entity chunking and entity extraction) is a subtask of information
extraction that seeks to locate and classify named entity mentions in
unstructured text into pre-defined categories such as the person
names, organizations, locations, medical codes, time expressions,
quantities, monetary values, percentages, etc.
There are already trained models for that, but most of them are for generic usage. For example in Python
nlp = spacy.load('en_core_web_sm')
doc = nlp('European authorities fined Google a record $5.1 billion on Wednesday for abusing its power in the mobile phone market and ordered the company to alter its practices')
print([(X.text, X.label_) for X in doc.ents])
The output is
('$5.1 billion', 'MONEY'),
Source of code: TowardsDataScience
In your case, you have to either train an NER yourself for phone specifications or find one that is available in public.