---
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  dataset_size: 27231293
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: odc-by
language:
- en
tags:
- manual
- narrative
size_categories:
- n<1K
---

# NarraDolma Gold — Human Annotations
 
The human-annotated gold standard behind NarraBert and NarraDolma. It contains
**400 passages** sampled from [Dolma](https://huggingface.co/datasets/allenai/dolma)
and annotated across all **11 narrative dimensions** plus event relations. This is
the validation backbone of the project: every automated label (LLM and NarraBert)
is measured against it.
 
- **Paper:** [arXiv:2606.19468](https://arxiv.org/abs/2606.19468)
- **Collection:** [Narratives in LLM Pretraining Data](https://huggingface.co/collections/teagrjohnson/narratives-in-llm-pretraining-data)
## What's in the dataset
 
Each row is a 3-sentence passage with its Dolma source and human labels.
 
| Group | Fields | Type |
|---|---|---|
| Agency | focalization, emotion, cognition, change_of_state, conflict | 1–5 Likert |
| Setting | concreteness, temporal_grounding, spatial_grounding, sensory | 1–5 Likert |
| Event relations | temporal_order, causal_relation | categorical (per event pair) |
 
- **Temporal order** labels: `span1_first`, `span2_first`, `simultaneous`, `same_event`, `too_hard_to_tell`.
- **Causal relation** labels: `direct_cause`, `enables`, `not_related` (collapsed to a binary causal / not-causal label for downstream analysis).
- Event-relation labels are annotated at the level of **adjacent event-trigger pairs**, not whole passages, so a passage may contribute multiple event-pair rows.
Provenance fields: `dolma_id`, `source`, `topic` (Common Crawl only), and a split
indicator (see below).
  
## Splits
 
For agency and setting, the 400 passages are divided into two **non-overlapping**
gold sets of 200:
 
- **Split A** — used to validate the LLM annotators.
- **Split B** — held out to evaluate NarraBert.
Event-relation annotations were too sparse to split, so event evaluation uses the
full annotated set at both stages.
 
## Annotation & agreement
 
One author annotated all 400 passages across the three tasks. For verification, a
second author annotated overlapping subsets (agency *N*=100, setting *N*=30, events
*N*=251) and an additional annotator participated in the setting task (*N*=70).
 
Inter-annotator agreement (per-dimension breakdowns in the paper appendix):
 
| Task | Agreement |
|---|---|
| Agency | mean α = 0.76 (0.69–0.80), MAE 0.62 |
| Setting | mean α = 0.70 (0.63–0.75), MAE 0.55 |
| Event relations | mean κ = 0.68, mean F1 = 0.91 |
 
Temporal ordering is the hardest dimension (κ = 0.60), in part because of severe
class imbalance among temporal relations.
 
## Intended use & caveats
 
- Designed as a **validation/evaluation set**, not a training set — it is small by
  design, reflecting the fine-grained, multi-month calibration effort behind it.
- Primary annotation used for model validation was conducted by a single author;
  agreement on some dimensions is modest.
## License & ethical considerations
 
Released under [ODC-By](https://opendatacommons.org/licenses/by/1-0/). Passages are
drawn from web-scraped Dolma and may include toxic, explicit, or personal content.
Each row carries the Dolma unique ID for rehydration. Please use for research and
auditing only.
 
## Citation
 
```bibtex
@misc{johnson2026narrative,
  title         = {Characterizing Narrative Content in Web-scale LLM Pretraining Data},
  author        = {Johnson, Teagan and Ash, Elliott and Piper, Andrew and Antoniak, Maria},
  year          = {2026},
  eprint        = {2606.19468},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2606.19468}
}
```