action namsor_pinyin_chinese_name {
  label: "Romanize the Chinese name to Pinyin, ex. 王晓明 -> Wang (surname) Xiaoming (given name)"
  provider: namsor
  method: GET
  path: "/api2/json/pinyinChineseName/{chineseName}"
  encoding: json
  input: {
    type: "object"
    properties: {
      chineseName: {
        type: "string"
      }
    }
    required: ["chineseName"]
    additionalProperties: false
  }
  output: {
    type: "object"
    properties: {
      firstLastName: {
        type: "object"
        description: "First / last name structure corresponding to the most likely parsing."
        properties: {
          firstName: {
            type: "string"
            description: "The first name (also known as given name)"
          }
          id: {
            type: "string"
          }
          lastName: {
            type: "string"
            description: "The last name (also known as family name, or surname)"
          }
          script: {
            type: "string"
          }
        }
      }
      id: {
        type: "string"
      }
      name: {
        type: "string"
        description: "The input name"
      }
      nameParserType: {
        type: "string"
        description: "Name parsing is addressed as a classification problem, for example FN1LN1 means a first then last name order."
        enum: ["FN1LN1", "LN1FN1", "FN1LN2", "LN2FN1", "FN1LNx", "LNxFN1", "FN2LN1", "LN1FN2", "FN2LN2", "LN2FN2", "FN2LNx", "LNxFN2", "FNxLN1", "LN1FNx", "FNxLN2", "LN2FNx", "FNxLNx", "LNxFNx"]
      }
      nameParserTypeAlt: {
        type: "string"
        description: "Second best alternative parsing. Name parsing is addressed as a classification problem, for example FN1LN1 means a first then last name order."
        enum: ["FN1LN1", "LN1FN1", "FN1LN2", "LN2FN1", "FN1LNx", "LNxFN1", "FN2LN1", "LN1FN2", "FN2LN2", "LN2FN2", "FN2LNx", "LNxFN2", "FNxLN1", "LN1FNx", "FNxLN2", "LN2FNx", "FNxLNx", "LNxFNx"]
      }
      score: {
        type: "number"
        format: "double"
        description: "Higher score is better, but score is not normalized. Use calibratedProbability if available. "
      }
      script: {
        type: "string"
      }
    }
  }
}