Datasets:
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.