**Short answer:** the dataset has no identifier column, one categorical dimension, four numeric metrics, and one timestamp, which is ingestion metadata rather than a business date. | Class | Columns | Basis | |---|---|---| | **Identifiers** | None | No column has a key role. The highest uniqueness score among the data columns is 0.26 (`petal_length`). Each row is one specimen, but nothing labels it. | | **Categorical dimension** | `species` (VARCHAR) | Semantic role is classifier and aggregation role is dimension. Its uniqueness score is 0.02, about 3 distinct values out of 150 rows, with 0 nulls. | | **Numeric metrics** | `sepal_length` (4.3–7.9), `sepal_width` (2.0–4.4), `petal_length` (1.0–6.9), `petal_width` (0.1–2.5) | All are DOUBLE, with semantic role and aggregation role both "measure" and 0 nulls. | | **Date/time** | `_ingestion_timestamp` (TIMESTAMP) | This is a load-time stamp, not a date describing the flower. The data has no observation date, so time-trend analysis isn't meaningful. | | **Lineage/metadata** | `_batch_id`, `_source_file`, `_source_system` (all VARCHAR) | These are pipeline columns. They are labels about the load, not descriptors of the flowers. | - **Profiling coverage:** the profiling result in my evidence covers only the five domain columns (`species` and the four measurements). The four underscore-prefixed columns are classified from their names and types on the dataset card. I did not profile their values. `_batch_id` could look like an identifier, but it would identify a load batch, not a specimen. - **Type label quirk:** the profiler tags the four measurements with a rough kind of "text" even though they are stored as DOUBLE. I treated them as numeric because the physical type and the measure roles both say so.