Lookup tables in Rasa

I am a little bit confused about the lookup tables in Rasa.

I am trying to extract the country name from the user message. I want to extract the country name and save it in slot, but it don’t work.

Note: I don’t provide any intents with those country names, should I provide or it can without?

I am using this configurations in my project.

# Configuration for Rasa NLU.
# https://rasa.com/docs/rasa/nlu/components/
language: en

  # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
  # If you'd like to customize it, uncomment and adjust the pipeline.
  # See https://rasa.com/docs/rasa/tuning-your-model for more information.
  - name: WhitespaceTokenizer
  - name: RegexFeaturizer
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: char_wb
    min_ngram: 1
    max_ngram: 4
  - name: DucklingEntityExtractor
    #url of the running duckling server
    url: http://localhost:8000
    # dimensions to extract
    dimensions: ["amount-of-money"]
    # allows you to configure the locale, by default the language is
    # used
    locale: "de_DE"
    # if not set the default timezone of Duckling is going to be used
    # needed to calculate dates from relative expressions like "tomorrow"
    timezone: "Europe/Berlin"
    # Timeout for receiving response from http url of the running duckling server
    # if not set the default timeout of duckling http url is set to 3 seconds.
    timeout: 3
  - name: RegexEntityExtractor
    # text will be processed with case insensitive as default
    case_sensitive: False
    # use lookup tables to extract entities
    use_lookup_tables: True
    # use regexes to extract entities
    use_regexes: True
    # use match word boundaries for lookup table
    "use_word_boundaries": True
  - name: DIETClassifier
    epochs: 100
    constrain_similarities: true
  - name: EntitySynonymMapper
  - name: ResponseSelector
    epochs: 100
    constrain_similarities: true
  - name: FallbackClassifier
    threshold: 0.3
    ambiguity_threshold: 0.1


From the docs:

Provide at least two annotated examples of the entity so that the model can register it as an entity at training time.