**Short answer:** the iris table has no identifier column. It has one categorical dimension, four numeric metrics, and one real timestamp, which is pipeline metadata rather than data about the flowers. **Categorical dimension (1)** - `species` is a VARCHAR that the profile marks as `classifier` / `dimension`, with no nulls. Its uniqueness score is 0.02, which fits the card's ~3 distinct values (setosa, versicolor, virginica). **Numeric metrics (4)** - `sepal_length` (DOUBLE, range 4.3–7.9) - `sepal_width` (DOUBLE, range 2.0–4.4) - `petal_length` (DOUBLE, range 1.0–6.9) - `petal_width` (DOUBLE, range 0.1–2.5) All four are marked `semantic_role = measure` and `aggregation_role = measure`, with zero nulls. The profile labels their `rough_kind` as "text", but the physical type is DOUBLE, so that label is a profiler quirk and they are genuinely numeric. **Identifiers (none among the data columns)** - No column has a `key_role`, and the highest uniqueness score is 0.26 (`petal_length`). An identifier would score close to 1.0, so none of these columns uniquely labels a specimen. - Each row is one flower, but there is no specimen ID, so rows can only be referred to by position. - The card lists `_batch_id`, `_source_file` and `_source_system` as ingestion-lineage columns. They identify where a row came from, not which flower it is. I did not profile them in this turn, so I am going by their names and the card. **Dates/times (1, technical only)** - `_ingestion_timestamp` (TIMESTAMP) records when the row was loaded. It says nothing about when the flowers were measured. - The data has no measurement-date column, so time-trend questions about the specimens can't be answered. **Summary by column** | Role | Columns | |---|---| | Identifier | none | | Categorical dimension | `species` | | Numeric metrics | `sepal_length`, `sepal_width`, `petal_length`, `petal_width` | | Date/time | `_ingestion_timestamp` (load time only) | | Lineage metadata (text) | `_batch_id`, `_source_file`, `_source_system` | The profile queries behind this were `analytics.column_overview`-style lookups on the five data columns (steps 0–2). The metadata column types come from the dataset card.