---
license: cc-by-4.0
task_categories:
- text-classification
- token-classification
language:
- es
tags:
- signed-graphs
- relation-extraction
- political-networks
- text2graph
- benchmark
pretty_name: text2signed-graph Gold Standard (Synthetic v2)
size_categories:
- n<1K
configs:
- config_name: relations_full
  data_files: relations_full.parquet
  default: true
- config_name: articles
  data_files: articles.parquet
- config_name: nodes
  data_files: nodes.parquet
- config_name: edges
  data_files: gold.parquet
---

# text2signed-graph — Gold Standard (Synthetic v2)

A synthetic benchmark for **extracting signed political relation graphs from news
articles**. Each relation is a directed, signed triple between political actors:
`(actor_u → act_type → actor_v, polarity)`, grounded in an evidence quote.

Built to evaluate `text2SG` extraction pipelines (NER → entity resolution → relation
extraction) in a **precision-first** setting, with planted distractors to penalize
over-extraction.

## Contents

| File | Rows | Description |
|---|---|---|
| **`relations_full.parquet`** ⭐ | **914** | **the signed graph, denormalized & human-readable** — `actor_u → act_type → actor_v, polarity` with actor names + article metadata. Start here. |
| `articles.parquet` | 282 | input — synthetic news articles (incl. distractors) |
| `nodes.parquet` | 1,579 | graph nodes (actors per article) — normalized form |
| `gold.parquet` | 914 | graph edges (by `union_id`) — normalized form |
| `split.json` | — | `train` (207) / `test` (75) article split + seed |
| `unions.json` | — | raw nested form of the nodes |

### `relations_full.parquet` schema (start here — viewer-friendly)
`article_id`, **`actor_u`**, **`act_type`**, **`actor_v`**, **`polarity`** (the signed
edge), `u_type`, `v_type`, `is_reactive`, `issue`, `evidence_quote`, `source`,
`dispute_type`, `title`, `dominio`, `dureza`, `es_distractor`. Each row is one gold edge
`actor_u → act_type → actor_v (polarity)` with everything joined in.

## The signed graph (the core)

Each article has a **gold signed graph**: `nodes.parquet` (actors) + `gold.parquet`
(signed edges between them). Join them on (`article_id`, `union_id` ↔ `u_from`/`u_to`).

### `nodes.parquet` schema (the actors)
`article_id`, **`union_id`** (e.g. `U7`), **`type`** (`roster_actor` = known political
figure / `institutional_actor` / `non_roster_actor`), **`canonical_name`**, `surfaces`
(alias list). 1,579 nodes total.

### `gold.parquet` schema (the signed edges)
`article_id`, **`u_from`**, **`u_to`** (→ `nodes.union_id`), `act_type` (e.g. `endorses`,
`attacks`, `allies_with`, `questions`…), **`polarity`** (`positive` / `negative` /
`neutral` — **the edge sign**), `is_reactive`, `issue`, `evidence_quote`, `source`
(`planted` / …), `n_inclusion_votes`, `dispute_type`.

### `articles.parquet` schema (the input)
`article_id`, `title`, `body`, `dominio` (topic), **`dureza`** (relation difficulty:
`explicita` / `mixta` / `oblicua`), **`es_distractor`** (distractor flag).

## Why synthetic

Real Chilean political news carry copyright (CC-BY-NC). This synthetic gold reproduces the
register, difficulty, and structure of real coverage — including **distractor articles and
near-miss relations** — so it can be shared openly as a reproducible benchmark.

## Evaluation

Score a predicted relation set against `gold.parquet` on the `test` split. The reference
extractor (`id15` / `gemini_champion_v2`) reaches **f0.5 ≈ 0.89** on detection (undirected),
precision-first. Distractors and `dispute_type` test robustness to over-extraction.

## Lineage

This gold standard was built and used to develop the **text2SG** extraction pipeline:

1. **[text2graph-evolve](https://github.com/bpalas/text2graph-evolve)** — evolutionary engine
   that optimizes extraction prompts/genomes against this gold (fitness = f0.5, precision-first).
   The champion extractors (`id15`, `gemini_champion_v2`) were evolved here.
2. **[chilean-political-dataset-signed-networks](https://github.com/bpalas/chilean-political-dataset-signed-networks)**
   — applies those champions at scale to build a signed political network of Chile
   (2014–2026): ~480k articles → 442k actor nodes, 2.5M signed edges.

So the flow is: **this gold → evolve the extractor → run it on real news → signed graph.**

## Citation

Part of the **text2SG** line of work on signed political networks. Built by Benjamín Palacios.