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splits:
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num_examples: 456
download_size: 16584563
dataset_size: 27231293
configs:
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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 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
- Collection: 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. 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
@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}
}