---
title: "GENOME — paper by Dell'Orto and Kommandeur (2026)"
description: "Event data on international relations depends on automated coding. 32 references. Among them: 0."
authors: ["Alessandro Dell'Orto", "Jesse Kommandeur"]
year: 2026
venue: "Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)"
source: "https://aclanthology.org/2026.eeuca-1.9/"
doi: "10.18653/v1/2026.eeuca-1.9"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/orto-2026-genome-new-geopolitical-event-methodology/"
---
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# GENOME: A New Geopolitical Event Methodology and Dataset using Large Language Models

**Authors:** Alessandro Dell'Orto and Jesse Kommandeur · **Year:** 2026 · **Venue:** [Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)](https://md.research.elamur.ai/free/en/venues/eeuca/) ([Workshop on Event Extraction and Understanding: Challenges and Applications 2026](https://md.research.elamur.ai/free/en/venues/eeuca/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eeuca-1.9/) · [PDF](https://aclanthology.org/2026.eeuca-1.9.pdf) · [DOI 10.18653/v1/2026.eeuca-1.9](https://doi.org/10.18653/v1/2026.eeuca-1.9) · [OpenAlex](https://openalex.org/W7166886536)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

Event data on international relations depends on automated coding. POLECAT stopped updating around August 2024, which left no current machine-coded dataset that tracks both hostile and cooperative interactions between states. The authors address this with GENOME, a dataset built from English newswire using an LLM pipeline. It follows the 16 event types of the PLOVER ontology and adds a Third Party role to the usual Actor–Recipient pair, so that entities which give context to multilateral events have a place. The pipeline has four stages: extraction, classification, entity normalization and deduplication. It uses OpenAI GPT models with one-shot prompts and schema-constrained outputs. No gold standard exists, so the authors compare GENOME with POLECAT over five shared months instead. The two agree well on conflict events. GENOME, however, records many more cooperative and especially verbal events, dates events by when they happened rather than when they were reported, and separates international organizations at a finer level. The work matters because it restores an openly released, continuously updated resource for conflict early-warning research.

## Key points

- **Corpus:** about 148,000 English articles from roughly 2,400 sources, covering January 2024 to January 2026. The comparison with POLECAT uses February–June 2024 (about 28,000 events).
- **Models:** gpt-4o-mini summarizes the first ~200 words of each article and infers the event date. A fine-tuned gpt-4.1-nano assigns PLOVER classes; its training used silver labels produced by gpt-5.1.
- **Entity normalization:**
  - Names are fuzzy-matched against 248 countries and 40 organizations, which cut unique country/IO entries by 81.7%.
  - Sentence-Transformer embeddings clustered with FAISS reduced agent names by 35.4%, to 32,624.
  - GENOME distinguishes bodies such as NATO, IMF, WTO, BRICS and ICC.
- **Limitations:**
  - Because all sources come from a single English-language provider, the Caucasus, Latin America and South/Southeast Asia are only thinly represented.
  - There is no gold standard for computing precision, recall or F1.
  - 4.4% of events have negative date lags.
  - The deduplication threshold (0.80) and the clustering steps have not been checked against human judgments.
- **Release:** the data and analysis scripts are available at github.com/HCSS-Data-Lab/Submission-GENOME.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

Also on the byline: [The Hague Centre for Strategic Studies](https://ror.org/05ttdrn83).

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## Cite

Cite as: Alessandro Dell'Orto and Jesse Kommandeur. 2026. GENOME: A New Geopolitical Event Methodology and Dataset using Large Language Models. Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{dellorto2026genome,
  title = {{GENOME: A New Geopolitical Event Methodology and Dataset using Large Language Models}},
  author = {Alessandro Dell'Orto and Jesse Kommandeur},
  booktitle = {Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.eeuca-1.9},
  url = {https://doi.org/10.18653/v1/2026.eeuca-1.9}
}
```

- [← previous GENIUS Keylog Corpus - a German High School Student Corpus with Keystroke Logging Data](https://md.research.elamur.ai/free/en/papers/schaller-2026-genius-keylog-corpus-german-high/)
- [next → GenomeQA: Benchmarking General Large Language Models for Genome Sequence Understanding](https://md.research.elamur.ai/free/en/papers/long-2026-genomeqa-benchmarking-general-large-language/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/31/)

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32 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*