<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: 'Figure 14 : Information theft via fake credit card verification using stolen branding Figure 15 : Information theft via fake credit card verification using stolen branding Some of the campaigns appear to have a wider reach based on bit.ly statistics like this one from October 13 , 2017 : Figure 16 : bit.ly statistics for an October 13 , 2017 campaign Over several days during the last three months , Proofpoint researchers observed campaigns using similar techniques targeting the banking customers of Raffeisen and Sparkasse .' [/INST] [Figure:O, 14:O, ::O, Information:O, theft:O, via:O, fake:O, credit:O, card:O, verification:O, using:O, stolen:O, branding:O, Figure:O, 15:O, ::O, Information:O, theft:O, via:O, fake:O, credit:O, card:O, verification:O, using:O, stolen:O, branding:O, Some:O, of:O, the:O, campaigns:O, appear:O, to:O, have:O, a:O, wider:O, reach:O, based:O, on:O, bit.ly:B-Indicator, statistics:O, like:O, this:O, one:O, from:O, October:O, 13:O, ,:O, 2017:O, ::O, Figure:O, 16:O, ::O, bit.ly:B-Indicator, statistics:O, for:O, an:O, October:O, 13:O, ,:O, 2017:O, campaign:O, Over:O, several:O, days:O, during:O, the:O, last:O, three:O, months:O, ,:O, Proofpoint:B-Organization, researchers:O, observed:O, campaigns:O, using:O, similar:O, techniques:O, targeting:O, the:O, banking:O, customers:O, of:O, Raffeisen:O, and:O, Sparkasse:O, .:O] </s>