---
license: cc0-1.0
task_categories:
- text-generation
language:
- en
tags:
- narrative
- long-form-generation
- knowledge-graph
- story-generation
size_categories:
- 1K<n<10K
---

# DSG State-Continuation

Training data for **graph-conditioned long-form fiction generation**: given the
narrative state a reader would hold after chapters 1..t-1, and a one-line brief
for chapter t, write chapter t.

Built from **215 public-domain novels** (Project Gutenberg, English fiction),
segmented into chapters. **6,876 examples.**

## Why the state is built this way

The state is not a summary and not a retrieval index. It is a revision-aware
assertion store built **causally** — at example *t* it contains only what a
reader could know from chapters 1..t-1 — and maintained by an explicit update
calculus that distinguishes three things append-only pipelines conflate:

| operation | when | effect |
|---|---|---|
| `ELABORATE` | later text specifies what earlier text left open | refined in place, nothing retracted |
| `SUPERSEDE` | the **story world** changed | validity interval closed, kept as history |
| `REVISE` | the **reader** was wrong; it was never true | retracted, and so is anything derived from it |

The `SUPERSEDE`/`REVISE` split is the operational form of the narratological
distinction between an event in the story and a disclosure in the telling. It is
decided by an inspectable lexicon — predicate mutability, evidential source,
revelation markers — never by a language model.

On 28 annotated novels this calculus holds **0.0%** of the state's slots
self-contradictory, against **37–42%** for an append-only store, at every model
size tested (1.5B / 3B / 7B).

## Fields

| field | description |
|---|---|
| `book_id`, `title`, `author` | Gutenberg source |
| `chapter` | 1-indexed chapter number |
| `state` | the graph state after chapters 1..t-1, as a bounded fact digest |
| `beat` | one-sentence brief for chapter t |
| `target` | the real chapter text (mean ~4,100 characters) |
| `target_chars` | length of the target |

3.2% of rows have an empty state — these are chapter 1, the cold start.

## Intended use

Fine-tuning a writer to *use* a maintained narrative state rather than merely
have one in context. The point is scaling: a state digest does not grow with the
story, while a transcript does.

## Limitations

- **English public-domain fiction**, mostly 19th–early 20th century. Prose style
  and naming conventions are period-specific.
- States are **extracted, not annotated**: produced by Qwen2.5-7B-Instruct and
  reconciled symbolically. They contain extraction noise, including invented
  predicates and occasional wrong attributions.
- Chapter segmentation is heuristic (headings, with a paragraph-block fallback).
- Splitting for training should be **by book**, not by row: chapters from one
  novel share state and leak across a random split.

## Source

Code, calculus and evaluation: the Dynamic Story Graph project.
Corpus: Project Gutenberg via `sedthh/gutenberg_english`.