Datasetpublic

brandburner/wolfhall-s01-narrative-kg

Wolf Hall - Narrative Knowledge Graph A rich narrative knowledge graph extracted from Wolf Hall screenplays using the Fabula pipeline. Contains characters, locations, objects, organizations, events, themes, and conflict arcs with full participation semantics and Graph Gravity importance tiers. Dataset Overview Metric Value Source database wolfhall.s01 Type Season database Episodes 6 Total nodes 2,691 Total edges 9,242 Schema version 1.1.0… See the full description on the dataset page: https://huggingface.co/datasets/brandburner/wolfhall-s01-narrative-kg.

sourceHugging Facecc-by-sa-4.0updated 7mo agoView on Hugging Face

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Dataset Card

Wolf Hall - Narrative Knowledge Graph

A rich narrative knowledge graph extracted from Wolf Hall screenplays using the Fabula pipeline. Contains characters, locations, objects, organizations, events, themes, and conflict arcs with full participation semantics and Graph Gravity importance tiers.

Dataset Overview

Metric	Value
Source database	`wolfhall.s01`
Type	Season database
Episodes	6
Total nodes	2,691
Total edges	9,242
Schema version	1.1.0
Exported	2026-02-24

Entity Breakdown

Type	Count
Act	18
Agent	254
ConflictArc	33
Episode	6
Event	514
Location	259
Object	322
Organization	95
PlotBeat	859
SceneBoundary	330
Writer	1

Graph Gravity Tiers

Tier	Count	Description
anchor	21	Main characters / key locations
planet	144	Recurring entities
asteroid	765	Minor / one-off entities

Relationship Types

AFFILIATED_WITH, BELONGS_TO_EPISODE, CALLBACK, CAUSAL, CHARACTER_CONTINUITY, CONTAINS_ACT, CONTAINS_BEAT, CONTAINS_SCENE, CREDITED_ON, EMOTIONAL_ECHO, ESCALATION, FORESHADOWING, INVOLVED_IN_ARC, INVOLVED_WITH, IN_EVENT, NARRATIVELY_FOLLOWS, OCCURS_IN, OWNS, PARTICIPATED_AS, PART_OF ... and 6 more

Files

File	Description
`nodes.parquet`	All graph nodes with properties
`edges.parquet`	All relationships with properties
`positions.parquet`	3D layout coordinates for visualization
`meta.json`	Dataset metadata and entity counts

Schema

Nodes (`nodes.parquet`)

Column	Type	Description
`node_id`	string	Unique node identifier (UUID)
`primary_label`	string	Node type (Agent, Location, Event, etc.)
`name`	string	Display name
`description`	string	Foundational description
`tier`	string (nullable)	Graph Gravity tier: anchor / planet / asteroid
`episode_count`	int (nullable)	Number of distinct episodes entity appears in
`first_episode_seq`	int (nullable)	First appearance episode
`last_episode_seq`	int (nullable)	Last appearance episode
`properties_json`	string	Full node properties as JSON

Edges (`edges.parquet`)

Column	Type	Description
`source_node_id`	string	Source node UUID
`target_node_id`	string	Target node UUID
`relationship_type`	string	Relationship type (e.g., PARTICIPATED_AS)
`properties_json`	string	Edge properties as JSON

Positions (`positions.parquet`)

Column	Type	Description
`node_id`	string	Node UUID
`x`, `y`, `z`	float	3D coordinates
`size`	float	Node size (Graph Gravity weighted)
`r`, `g`, `b`	int	RGB color by entity type
`community`	int	Louvain community index
`tier`	string (nullable)	Graph Gravity tier

Usage

python

from datasets import load_dataset
import pandas as pd

# Load from HuggingFace
ds = load_dataset("brandburner/wolfhall-s01-narrative-kg")

# Or load parquet directly
nodes = pd.read_parquet("nodes.parquet")
edges = pd.read_parquet("edges.parquet")

# Filter to anchor characters
anchors = nodes[(nodes['primary_label'] == 'Agent') & (nodes['tier'] == 'anchor')]

# Build a NetworkX graph
import networkx as nx
G = nx.DiGraph()
for _, n in nodes.iterrows():
    G.add_node(n['node_id'], label=n['primary_label'], name=n['name'])
for _, e in edges.iterrows():
    G.add_edge(e['source_node_id'], e['target_node_id'], type=e['relationship_type'])

Citation

bibtex

@misc{fabula_wolfhall_s01,
  title = {Wolf Hall Narrative Knowledge Graph},
  author = {Fabula Pipeline},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/datasets/brandburner/wolfhall-s01-narrative-kg}}
}

License

CC BY-SA 4.0