> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/zingg/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/zingg/versions/enterprise-v0.9.1/explainoutput.md). # Explanation of Matches [Zingg Enterprise Feature](#user-content-fn-1)[^1] Entity resolution is more effective when you can observe how clusters form, connect, and evolve across runs. Once you have run a `match` or `runIncremental` phase, you may like to look under the hood to understand how a cluster got formed and which records matched with each other and which did not. This insight helps with the following: * Model Training and Validation: Quickly check if results match expectations. * Traceability: See exactly how clusters are formed over time. * Governance: Explain results to business stakeholders. * Human-in-the-loop Reviews: Share pair-level evidence with domain experts. * Operational Confidence: Build trust in production pipelines. ## Important Notes The `explain` phase currently focuses on **probabilistic matches** between records. Please note the following: * **Deterministic matches** are not included in the explain output at this time. These matches are based on exact rules and can be reviewed separately through your match results. * Some clusters may not generate explain output, particularly those formed primarily through deterministic matching rules. * **Empty results** will occur if the specified `ZINGG_ID` does not exist in the cluster data. Please verify the cluster ID exists in your match output before running explain. * For comprehensive cluster analysis, we recommend reviewing the primary match output alongside the explain output to get the complete picture of both deterministic and probabilistic relationships. This focused approach allows the explain phase to efficiently highlight the machine learning model's decision-making process for probabilistic matches, which typically require the most scrutiny and validation. [^1]: Zingg Enterprise is the suite of proprietary products licensed by Zingg. Please refer to for individual tier features. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.zingg.ai/zingg/versions/enterprise-v0.9.1/explainoutput.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.