{
    "slots": [
        {
            "scope": "dataset",
            "slot": "nature",
            "value": {
                "nature": "observational"
            },
            "evidence": {
                "reasoning": "Rows are physical measurements of iris flowers (sepal and petal dimensions) from the UCI ML Kaggle dataset. Nobody here operates a process that produces them."
            }
        },
        {
            "scope": "dataset",
            "slot": "decisions_served",
            "value": {
                "decisions": [
                    "Which morphological features (petal vs. sepal dimensions) best separate the three Iris species, and so which to use in a classifier",
                    "Whether a simple threshold or rule on petal size is enough to identify a species, or a multivariate model is needed",
                    "Which classification or clustering algorithm to choose when benchmarking on a small, well-separated multi-class problem"
                ]
            },
            "evidence": {
                "reasoning": "Four numeric measures plus a three-value Species label across 150 rows. The Species rollup (Iris_by_Species) points to per-species comparison and classification use."
            }
        },
        {
            "scope": "dataset",
            "slot": "levers",
            "value": {
                "levers": []
            },
            "evidence": {
                "reasoning": "The data records measured flower traits. No operator controls them and no actionable business lever appears in the columns."
            }
        },
        {
            "scope": "dataset",
            "slot": "audience",
            "value": {
                "audience": [
                    "data scientist",
                    "machine learning student or educator",
                    "botanist or taxonomist"
                ]
            },
            "evidence": {
                "reasoning": "A classic Kaggle/UCI teaching dataset for classification, clustering and exploratory analysis."
            }
        },
        {
            "scope": "table:Iris",
            "slot": "role",
            "value": {
                "role": "fact"
            },
            "evidence": {
                "reasoning": "The table is a set of per-flower measurement observations with four numeric measures. It has no children and is not a lookup, so fact fits better than the DIMENSION table_type."
            }
        },
        {
            "scope": "table:Iris",
            "slot": "grain",
            "value": {
                "unit": "one row per measured iris flower specimen",
                "key_columns": [
                    "Id"
                ]
            },
            "evidence": {
                "reasoning": "Id is unique across all 150 rows and is the primary key."
            }
        },
        {
            "scope": "table:Iris",
            "slot": "temporal_posture",
            "value": {
                "posture": "atemporal; the only timestamp is ingestion metadata",
                "columns": [
                    "_ingestion_timestamp"
                ]
            },
            "evidence": {
                "reasoning": "_ingestion_timestamp has a single distinct value, so it records the load time and not when measurements were taken. The measurements carry no time dimension."
            }
        },
        {
            "scope": "table:Iris",
            "slot": "column_bindings",
            "value": {
                "bindings": [
                    {
                        "column": "Id",
                        "binding": "identifier"
                    },
                    {
                        "column": "SepalLengthCm",
                        "binding": "measure"
                    },
                    {
                        "column": "SepalWidthCm",
                        "binding": "measure"
                    },
                    {
                        "column": "PetalLengthCm",
                        "binding": "measure"
                    },
                    {
                        "column": "PetalWidthCm",
                        "binding": "measure"
                    },
                    {
                        "column": "Species",
                        "binding": "category"
                    },
                    {
                        "column": "_ingestion_timestamp",
                        "binding": "temporal"
                    },
                    {
                        "column": "_batch_id",
                        "binding": "identifier"
                    },
                    {
                        "column": "_source_file",
                        "binding": "category"
                    },
                    {
                        "column": "_source_system",
                        "binding": "category"
                    }
                ]
            },
            "evidence": {
                "reasoning": "_batch_id is a single-value batch identifier, so it is rebound from category to identifier. The other derived bindings are kept."
            }
        }
    ]
}