class_docstr: predicts  classes for string-representation of sentence
class_docstr_tok: ["predicts","classes","for","string-representation","of","sentence"]
code: def _build_merge_tuple(
      self,
      current_token,
      current_tag,
      start=None,
      end=None,
      prob_list=[],
  ):
      entry = [current_token, current_tag]
      if start is not None and end is not None:
          entry.append((start, end))
      if prob_list:
          entry.append(np.mean(prob_list))
      return tuple(entry)
  
file_path: ktrain/ktrain/text/ner/predictor.py
imports: from ...None import utils
  from ...imports import *
  from ...predictor import Predictor
  from ..None import textutils
  from .preprocessor import NERPreprocessor
label_desc: Use image node_id 5 for calling the NERPredictor obj's underlying member method code with example usage: obj._build_merge_tuple(current_token, current_tag, start, end, prob_list) and returns: tuple