---
id: concepts.dimensions-ontology
title: Dimensions and ontology
summary: Learn how dimensions and ontology values define the coverage space for a Teammately project.
kind: concept
product_area: object_model
status: stable
updated: 2026-09-07
canonical: /docs/concepts/dimensions-and-ontology
---

# Dimensions and ontology

## Definition

Dimensions and ontology describe how Teammately classifies cases into behavior segments that humans can reason about. Dimensions name the axes that matter, while ontology values provide the controlled labels used for coverage planning, case review, and result analysis.

## Why it matters

This matters because aggregate benchmark results can hide an unsafe gap. A candidate may pass common cases while missing a stale-source segment, a boundary condition, a product tier, or a policy exception that reviewers care about.

## Coverage vocabulary check

| Good coverage vocabulary does... | Weak vocabulary does... |
| --- | --- |
| Names behavior slices that change judgment or risk. | Uses labels that only describe where the case came from. |
| Keeps ontology values consistent enough for comparison. | Lets free-form tags drift until segment results are meaningless. |
| Makes missing or thin segments visible before review. | Treats a large case count as representative coverage. |

## Where it appears in the product

Create and maintain Dimensions and ontology values under **Coverage Facets → Dimensions and Ontology**. Benchmark Datasets uses them to inspect representation, while Benchmark Evaluations can group existing evidence by supported Coverage Facets. The Case Pool and Case Review surfaces use the same vocabulary when classifying and preparing Cases.

## Artifacts it affects

Dimensions affect Case classification, Benchmark Dataset representation, Coverage Stories, synthesis targets, Case Review, and result grouping. Changing the current vocabulary does not rewrite the labels or interpretation of historical Snapshots and Runs.

{% example-demo title="Stale-source segment" %}
An enterprise-search team adds a **Source freshness** Dimension with current, superseded, and unknown values. Benchmark Datasets then reveals that superseded-source Cases are thinly represented. The team prepares additional Cases, reviews their classifications, and creates a new Snapshot before using that segment in evaluation interpretation.
{% /example-demo %}

## Related workflows

{% related-card-grid title="Related workflows" %}
- [Generate a dimension schema](/docs/coverage-engineering/generate-dimension-schema)
- [Inspect Dataset representation](/docs/benchmark-datasets/representation)
- [Review prepared Cases](/docs/coverage-management/case-review)
- [Diagnose classification](/docs/troubleshooting/dimension-classification)
- [Refresh changed coverage](/docs/coverage-engineering/coverage-refresh)
{% /related-card-grid %}

## Related reference pages

{% related-card-grid title="Related reference pages" %}
- [Coverage Dimensions](/docs/object-model/coverage-dimensions)
- [Ontology](/docs/object-model/ontology)
- [Project Topics](/docs/coverage-engineering/project-topics)
- [Case Construction Patterns](/docs/coverage-engineering/case-construction-patterns)
{% /related-card-grid %}

## Source confidence

Code-backed: Dimension types and the Dimensions and Ontology surfaces define the project vocabulary and editable fields; Benchmark Dataset Representation shows how that vocabulary is used to inspect selected Cases.