> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/sdgm/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/sdgm/_mcp/server.

# Glossary

> Definitions of Kumo Relational data, graph, prediction, and configuration terms

Use this glossary for terms that have a specific meaning in Kumo Relational and the NVIDIA Kumo Relational Client. Follow the links for procedures, examples, and complete reference information.

## A

**Anchor timestamp.** The reference time associated with a prediction or context example. Kumo Relational uses the time boundary to determine which historical events are available to the prediction. See [Configuration](/rfm/configuration#time-controls).

## C

**Context example.** A labeled example supplied to Kumo Relational at prediction time. Kumo Relational can derive context examples from a PQL query or accept them directly through `predict_task()`.

**Custom context.** Context and prediction entities supplied as pandas DataFrames through `predict_task()`, instead of context examples derived from a PQL query. See [Predict with Custom Context](/rfm/custom-context).

## D

**Data type.** The physical representation of a column, such as integer, floating point, string, Boolean, timestamp, or list. See [Data Types and Semantic Types](/rfm/data-types).

## E

**Entity table.** A table representing objects for which Kumo Relational can make predictions. An entity table typically has a stable, unique primary key.

**End-time column.** Table metadata identifying when a row stops being valid. For the prediction entity table, it defines the exclusive end of the row's validity interval. See [Time and End-Time Columns](/rfm/time-columns).

**Event table.** A table containing time-dependent activities or transactions related to one or more entity tables. Its time column identifies when each event became available to the prediction workflow.

**Explanation.** The object returned when `explain=True`. It contains the prediction and structured attribution, and can include cohort analysis, subgraphs, and a natural-language summary. See [Prediction Explainability](/rfm/prediction-explainability).

## F

**Fan-out.** The maximum number of neighbors sampled at one graph hop. Each value in `num_neighbors` sets the fan-out for the corresponding hop. See [Neighborhood sampling](/rfm/configuration#neighborhood-sampling).

**Foreign key.** A column whose values refer to the primary key of another graph table. Kumo Relational relationships connect foreign keys to their destination entity tables.

## H

**Hop.** One relationship traversal in the relational neighborhood around a prediction entity. The number of entries in `num_neighbors` determines the number of sampled hops.

## I

**Indices.** Entity primary-key values passed through the `indices` argument to `predict()`. They select the entities to score and override entity IDs embedded in the PQL query. See [Make Predictions](/rfm/make-predictions#3-predict-for-multiple-entities).

**Inference configuration.** Task-specific model settings passed through `inference_config`, including ensemble and output settings. See [Inference configuration](/rfm/configuration#inference-configuration).

## N

**Neighborhood.** Relational context sampled around a prediction entity. Its depth and maximum fan-out are controlled by `num_neighbors`.

**NVIDIA NIM.** A deployed inference service that hosts Kumo Relational and exposes its endpoint. The SDK connects to the NIM and sends prediction requests; it does not deploy or run the model itself. See [Deploy, Install, and Connect](/rfm/sdk-getting-started).

## P

**Prediction entity.** The entity for which Kumo Relational returns a prediction. In PQL, the entity is identified by the table key in the `FOR` or `FOR EACH` clause.

**Prediction horizon.** The future interval covered by a temporal prediction target. The horizon is expressed by the start, end, and time unit in a PQL aggregation. See [Query Kumo Relational](/rfm/querying-rfm#target--entity--horizon).

**Predictive Query Language (PQL).** The language used to describe a predictive task over a relational graph. A runnable Kumo Relational query identifies a target and the entity or entities to score. See [Write Predictive Queries](/rfm/writing-predictive-queries).

**`RelationalClient`.** The public SDK client that owns the HTTP connection to one NIM endpoint. Bind a graph with `client.relational(graph)` before making Kumo Relational predictions. See [Python SDK API](/rfm/python-api-reference).

**Primary key.** A non-null, unique column that identifies each row in a graph table. Relationships from other tables can refer to this key.

## R

**Relational graph.** The tables, table metadata, and relationships Kumo Relational uses to sample predictive context. See [Create a Graph](/rfm/graph-creation).

**Relationship.** A graph connection from a foreign key in an event or junction table to the primary key of an entity table. See [Define Relationships](/rfm/graph-definitions).

**Run mode.** A prediction preset that controls the maximum number of context examples and the default neighborhood fan-out. See [Run modes and defaults](/rfm/configuration#run-modes-and-defaults).

## S

**Semantic type.** Metadata describing how Kumo Relational should interpret a column, such as `ID`, `numerical`, `categorical`, `text`, `timestamp`, or `sequence`. See [Data Types and Semantic Types](/rfm/data-types).

## T

**Target.** The value or event to predict. In PQL, the target can be a column, a future aggregation, or a condition applied to an aggregation.

**Task type.** The prediction category, such as classification, regression, forecasting, ranking, or link prediction. See [Prediction Types](/rfm/prediction-types).

**Time column.** Table metadata identifying when an event became available to the prediction workflow. A datetime column does not automatically become the table's time column. See [Time and End-Time Columns](/rfm/time-columns).