<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Gathered file Type Description lock Text Implant log ldata sqlite3 Location data based on network ( cell_id ) gdata sqlite3 Location data based on GPS coordinates sdata sqlite3 SMS messages f.db sqlite3 Facebook messages v.db sqlite3 Viber messages w.db sqlite3 WhatsApp messages Among the other data gathered were SMS banking messages that revealed an account with a balance of more than US $ 10,000.But as far as we know , the attacker behind this campaign is not interested in stealing the victims ’ money' [/INST] [Gathered:O, file:O, Type:O, Description:O, lock:O, Text:O, Implant:O, log:O, ldata:O, sqlite3:O, Location:O, data:O, based:O, on:O, network:O, (:O, cell_id:O, ):O, gdata:O, sqlite3:O, Location:O, data:O, based:O, on:O, GPS:O, coordinates:O, sdata:B-Indicator, sqlite3:I-Indicator, SMS:O, messages:O, f.db:B-Indicator, sqlite3:I-Indicator, Facebook:B-System, messages:O, v.db:B-Indicator, sqlite3:I-Indicator, Viber:B-System, messages:O, w.db:B-Indicator, sqlite3:I-Indicator, WhatsApp:B-System, messages:O, Among:O, the:O, other:O, data:O, gathered:O, were:O, SMS:O, banking:O, messages:O, that:O, revealed:O, an:O, account:O, with:O, a:O, balance:O, of:O, more:O, than:O, US:O, $:O, 10,000.But:O, as:O, far:O, as:O, we:O, know:O, ,:O, the:O, attacker:O, behind:O, this:O, campaign:O, is:O, not:O, interested:O, in:O, stealing:O, the:O, victims:O, ’:O, money:O] </s>