Start with one operation

{
  "model": "openai/gpt-5.6-sol",
  "messages": [
    {
      "role": "system",
      "content": "Extract every explicitly stated employment relationship as jobs with name, company and position. Keep each person's relationships separate, including former roles. Copy names and titles from the source. Use null for missing fields. Never infer a company from proximity alone."
    },
    {
      "role": "user",
      "content": "Ada Chen is an engineer at Acme and a director at Beta. Ben Park formerly worked as an analyst at Acme; his current employer is not stated."
    }
  ],
  "response_format": {
    "type": "json_schema",
    "json_schema": {
      "name": "result",
      "schema": {
        "type": "object",
        "properties": {
          "jobs": {
            "type": "array",
            "maxItems": 32,
            "items": {
              "type": "object",
              "properties": {
                "name": {
                  "type": "string"
                },
                "company": {
                  "type": [
                    "string",
                    "null"
                  ]
                },
                "position": {
                  "type": [
                    "string",
                    "null"
                  ]
                }
              },
              "required": [
                "name",
                "company",
                "position"
              ],
              "additionalProperties": false
            }
          }
        },
        "required": [
          "jobs"
        ],
        "additionalProperties": false
      }
    }
  }
}

{
  "inputs": {
    "mentions": [
      {
        "id": "m1",
        "text": "Acme Labs"
      },
      {
        "id": "m2",
        "text": "Unknown Works"
      }
    ],
    "catalog": [
      {
        "id": "acme",
        "name": "Acme Research",
        "aliases": [
          "Acme Labs"
        ]
      },
      {
        "id": "beta",
        "name": "Beta Labs",
        "aliases": []
      }
    ]
  },
  "calls": [
    {
      "id": "link",
      "model": "openai/gpt-5.6-sol",
      "messages": [
        {
          "role": "system",
          "content": "Link each supplied mention to a canonical organization in the supplied catalog. Match exact names or aliases, using context when needed. If multiple organizations fit, use ambiguous and null. If none fit, use no_match and null. Return links in mention order. Never invent IDs."
        },
        {
          "role": "user",
          "content": "Mentions: {{mentions}}\nCatalog: {{catalog}}"
        }
      ],
      "response_format": {
        "type": "json_schema",
        "json_schema": {
          "name": "links",
          "schema": {
            "type": "object",
            "properties": {
              "links": {
                "type": "array",
                "maxItems": 32,
                "items": {
                  "type": "object",
                  "properties": {
                    "mention_id": {
                      "type": "string"
                    },
                    "candidate_id": {
                      "type": [
                        "string",
                        "null"
                      ]
                    },
                    "status": {
                      "type": "string",
                      "enum": [
                        "matched",
                        "ambiguous",
                        "no_match"
                      ]
                    }
                  },
                  "required": [
                    "mention_id",
                    "candidate_id",
                    "status"
                  ],
                  "additionalProperties": false
                }
              }
            },
            "required": [
              "links"
            ],
            "additionalProperties": false
          }
        }
      }
    }
  ],
  "bindings": [
    {
      "call": "link",
      "message": 1,
      "placeholder": "{{mentions}}",
      "source": "input#/mentions"
    },
    {
      "call": "link",
      "message": 1,
      "placeholder": "{{catalog}}",
      "source": "input#/catalog"
    }
  ],
  "output_schema": {
    "type": "object",
    "properties": {
      "links": {
        "type": "array",
        "maxItems": 32,
        "items": {
          "type": "object",
          "properties": {
            "mention_id": {
              "type": "string"
            },
            "candidate_id": {
              "type": [
                "string",
                "null"
              ]
            },
            "status": {
              "type": "string",
              "enum": [
                "matched",
                "ambiguous",
                "no_match"
              ]
            }
          },
          "required": [
            "mention_id",
            "candidate_id",
            "status"
          ],
          "additionalProperties": false
        }
      }
    },
    "required": [
      "links"
    ],
    "additionalProperties": false
  }
}

{
  "inputs": {
    "message": "Lunch USD 42, receipt attached. Taxi USD 18.50, receipt attached. Wine USD 12, receipt attached. Coffee USD 4, no receipt."
  },
  "calls": [
    {
      "id": "facts",
      "model": "openai/gpt-5.6-sol",
      "messages": [
        {
          "role": "system",
          "content": "Apply this expense policy: only USD expenses with receipts qualify; alcohol never qualifies; reimburse meals up to USD 30 each, transport in full. Return amounts as decimal strings for eligible reimbursable amounts only, preserving source order. Do not include unsupported categories."
        },
        {
          "role": "user",
          "content": "{{message}}"
        }
      ],
      "response_format": {
        "type": "json_schema",
        "json_schema": {
          "name": "amounts",
          "schema": {
            "type": "object",
            "properties": {
              "amounts": {
                "type": "array",
                "items": {
                  "type": "string"
                }
              }
            },
            "required": [
              "amounts"
            ],
            "additionalProperties": false
          }
        }
      }
    },
    {
      "id": "total",
      "model": "openai/gpt-5.6-sol",
      "messages": [
        {
          "role": "system",
          "content": "Sum the supplied reimbursable amounts exactly. Return JSON with total as a decimal string. Empty amounts sum to zero."
        },
        {
          "role": "user",
          "content": "{{amounts}}"
        }
      ],
      "response_format": {
        "type": "json_schema",
        "json_schema": {
          "name": "total",
          "schema": {
            "type": "object",
            "properties": {
              "total": {
                "type": "string"
              }
            },
            "required": [
              "total"
            ],
            "additionalProperties": false
          }
        }
      }
    }
  ],
  "bindings": [
    {
      "call": "facts",
      "message": 1,
      "placeholder": "{{message}}",
      "source": "input#/message"
    },
    {
      "call": "total",
      "message": 1,
      "placeholder": "{{amounts}}",
      "source": "facts#/amounts"
    }
  ],
  "output_schema": {
    "type": "object",
    "properties": {
      "total": {
        "type": "string"
      }
    },
    "required": [
      "total"
    ],
    "additionalProperties": false
  }
}