code: def named_entities(s: TextSeries, package="spacy") -> pd.Series:
      """
      Return named-entities.
  
      Return a Pandas Series where each row contains a list of tuples
      with information about the named entities in the row's document.
  
      Tuple: (`entity'name`, `entity'label`, `starting character`,
              `ending character`)
  
      Under the hood, `named_entities` makes use of `Spacy name entity
      recognition <https://spacy.io/usage/linguistic-features#named-entities>`_
  
      List of labels:
       - `PERSON`: People, including fictional.
       - `NORP`: Nationalities or religious or political groups.
       - `FAC`: Buildings, airports, highways, bridges, etc.
       - `ORG` : Companies, agencies, institutions, etc.
       - `GPE`: Countries, cities, states.
       - `LOC`: Non-GPE locations, mountain ranges, bodies of water.
       - `PRODUCT`: Objects, vehicles, foods, etc. (Not services.)
       - `EVENT`: Named hurricanes, battles, wars, sports events, etc.
       - `WORK_OF_ART`: Titles of books, songs, etc.
       - `LAW`: Named documents made into laws.
       - `LANGUAGE`: Any named language.
       - `DATE`: Absolute or relative dates or periods.
       - `TIME`: Times smaller than a day.
       - `PERCENT`: Percentage, including ”%“.
       - `MONEY`: Monetary values, including unit.
       - `QUANTITY`: Measurements, as of weight or distance.
       - `ORDINAL`: “first”, “second”, etc.
       - `CARDINAL`: Numerals that do not fall under another type.
  
      Examples
      --------
      >>> import texthero as hero
      >>> import pandas as pd
      >>> s = pd.Series("Yesterday I was in NY with Bill de Blasio")
      >>> hero.named_entities(s)[0] # doctest: +NORMALIZE_WHITESPACE
      [('Yesterday', 'DATE', 0, 9), ('NY', 'GPE', 19, 21),
       ('Bill de Blasio', 'PERSON', 27, 41)]
      """
      entities = []
  
      nlp = en_core_web_sm.load(disable=["tagger", "parser"])
  
      # nlp.pipe is now 'ner'
      for doc in nlp.pipe(s.astype("unicode").values, batch_size=32):
          entities.append(
              [
                  (ent.text, ent.label_, ent.start_char, ent.end_char)
                  for ent in doc.ents
              ]
          )
  
      return pd.Series(entities, index=s.index)
  
file_path: texthero/texthero/nlp.py
imports: import spacy
  import pandas
  from nltk.stem import PorterStemmer, SnowballStemmer
  from texthero._types import TextSeries, InputSeries
label_desc: Use image node_id 1 for calling a global function with example usage: named_entities(s, package) and returns: pd