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REVIEW 4 major objections 6 minor 53 references

Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read WORLDREP, an LLM-built dataset of 44,706 news articles, labels country-pair relations that match political-science experts 77.4% of the time, versus GDELT's 30.6%.

desk verdict WORLDREP is a real dataset contribution, but the headline GDELT comparison is inflated and the forecasting benchmark is partially circular. read the letter →

arxiv 2411.14042 v1 pith:MM625IXY submitted 2024-11-21 cs.CL cs.AI

Daehoon Gwak , Junwoo Park , Minho Park , Chaehun Park , Hyunchan Lee , Edward Choi , Jaegul Choo This is my paper · ORCID
classification cs.CLcs.AI
keywords WORLDREPinternationaleventpredictionLLM-baseddataannotationself-correctingscratchpadGDELTrelationshiplabelinggeopoliticalforecastingdatasetqualityevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces WORLDREP, a dataset of 44,706 news articles in which country pairs are labeled with a continuous conflict–cooperation score plus an 'unknown' category, generated by large language models guided by a self-correcting scratchpad. The central claim is that these labels are substantially more reliable than the widely used GDELT database, and the paper supports this by comparing both label sets against judgments made by political-science experts on 1,030 articles. On that comparison, WORLDREP agrees with the experts 77.4% of the time overall—84.8% on conflict, 70.6% on cooperation, and 73.7% on unknown—whereas GDELT agrees only 30.6% of the time. If this holds, WORLDREP gives researchers a more trustworthy training signal and benchmark for predicting future international relations from news text, and its automated pipeline means the labeling can be extended to newer articles without the usual expert-labeling bottleneck.

What carries the argument

The central mechanism is a structured scratchpad prompt: each annotation is produced in steps—extract, verify, explain disagreements, correct—so the model can catch its own mistakes before the label is recorded. Country extraction and relationship scoring each use this pattern, and each relationship pair is scored five times with a majority vote on 'unknown' and averaging of the remaining scores. This yields a continuous 0-to-1 score (0 = full cooperation, 1 = full conflict) that is thresholded into cooperation, conflict, and unknown classes. The paper credits this self-correcting, ensembled scoring for both the higher agreement with experts and the improved country-extraction F1.

What would settle it

Have a second, independent panel of international-relations experts label a random sample of WORLDREP articles, and report per-label agreement and inter-annotator agreement; if the overall agreement with the original labels falls well below 77.4%, or the expert panel itself disagrees, the headline quality claim fails.

Watch

Extended reading notes

Core claim

The paper's discovery is that an LLM-based annotation pipeline with built-in verification and correction can produce relationship labels that align with domain-expert judgment far more closely than GDELT's rule-based labels, while also capturing multilateral relations and an 'unknown' category that GDELT lacks. The claim is established through a direct comparison on a shared test set: expert-labeled relationships in 1,030 articles, where WORLDREP attains 77.4% overall agreement versus GDELT's 30.6%. The paper further shows that classifiers trained on WORLDREP labels and evaluated on expert labels reach an average accuracy of 0.854, versus 0.469 for classifiers trained on GDELT labels, and that the self-correcting extraction step raises country-identification F1 from 0.825 to 0.963. The authors also provide a zero-shot benchmark in which frontier LLMs predict future country-pair relations from recent news, with top accuracy around 61%.

Load-bearing premise

The central comparison treats the labels that one professor and three graduate students assigned to 1,030 selected articles as an unbiased gold standard for all 44,706 articles, without reporting inter-annotator agreement.

Editorial extensions

If this is right

  • WORLDREP can serve as a training set for models that forecast whether a country pair will be in conflict, cooperation, or an indeterminate state at a future date.
  • The automated pipeline makes it feasible to refresh the dataset continuously, replacing the expensive expert-labeling bottleneck with LLM scoring that has been validated against experts.
  • The 'unknown' category lets models avoid forced binary predictions, which should reduce spurious conflict or cooperation classifications on ambiguous news.
  • Because labels are continuous, downstream systems can treat relationship intensity as a graded signal rather than a binary category.
  • The published expert-labeled test set gives the community a stable ground truth for comparing future event-prediction models.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next validation step, not performed in the paper, is to measure inter-annotator agreement among the experts; without it, the 77.4% agreement rate conflates label quality with expert consensus.
  • The 1,030-article expert subset was chosen for having full GDELT annotations, so it may over-represent clear-cut bilateral events; the quality gap on the full, more multilateral corpus could be different.
  • The same scratchpad-plus-ensemble recipe could be transferred to other annotation domains that need graded, multi-subject labels, such as economic or public-health event tracking.
  • If the 45–61% zero-shot accuracy on the future-prediction benchmark is representative, an immediate research target is context retrieval: feeding models the most relevant past articles rather than a fixed recent window.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper introduces WORLDREP, a dataset of 44,706 news articles annotated with pairwise country-relationship scores (cooperation-to-conflict, plus an 'Unknown' category) generated by GPT-4 using a two-stage scratchpad with verification and self-correction. The authors validate label quality against domain-expert annotations on 1,030 articles, report that WORLDREP agrees with experts at 77.4% overall versus GDELT's 30.6%, and show that classifiers trained on WORLDREP labels outperform those trained on GDELT labels when evaluated on expert-labeled test data. They then propose a zero-shot LLM benchmark for forecasting future country-pair relationships, reporting 45-61% accuracy across nine LLMs.

Significance. If the validation claims hold, WORLDREP would be a useful resource for text-based international event prediction, addressing two real limitations of GDELT: multilateral country extraction and a nuanced relationship label with an explicit 'Unknown' class. The paper ships the dataset, expert labels, and automation code, and the expert-annotation comparison in Section 3.2 is a non-circular, appropriately designed transfer experiment. The self-correcting scratchpad's measured improvement in country-extraction F1 (0.825 to 0.963 against expert labels, Table 3) is a concrete and credible contribution. However, the headline quality comparison in Table 2 is structurally unfair to GDELT, the expert gold standard is small and non-random, and the future-event benchmark in Section 4.1 uses the paper's own LLM labels as ground truth, so the forecasting numbers should not be read as externally validated accuracy.

major comments (4)
  1. [Section 3.1, Table 2] The aggregate agreement comparison of 77.4% versus 30.6% is structurally unfair because GDELT has no 'Unknown' label: every expert-labeled 'Unknown' pair is automatically counted as a GDELT error, while WORLDREP can score 73.7% on those same pairs. The authors acknowledge GDELT lacks 'Unknown' in the caption but still present the aggregate as the headline result and repeat it in the abstract. The fairer two-class comparison in Section 3.2 (restricting to pairs experts labeled Conflict or Cooperation) still shows a gap, but the paper should report that restricted agreement as the primary quality comparison, or give GDELT an explicit abstention option, and clearly delabel the aggregate table.
  2. [Section 3.1 and Appendix B.1] The expert validation uses only 1,030 articles selected because GDELT had fully annotated samples, which is not a random sample of the 44,706-article corpus, and the labels come from three graduate students supervised by one professor with no inter-annotator agreement reported. As stated, the 77.4% agreement rate cannot be shown to generalize to the full dataset. The authors should report per-annotator agreement (e.g., Cohen's or Fleiss' kappa), describe how disagreements were adjudicated, and either add a random-sample expert evaluation or explicitly present the current numbers as a non-random, limited pilot rather than a corpus-wide validation.
  3. [Section 3.1, score-to-class thresholds] The threshold binning (0.0-0.25 cooperation, 0.25-0.75 unknown, 0.75-1.0 conflict) is introduced without justification or sensitivity analysis. Since both Table 2 and the downstream classification experiments depend on these boundaries, and since changing the boundaries could shift agreement rates for both WORLDREP and GDELT, the authors should either justify the thresholds from the expert label distribution or report how agreement changes under reasonable alternative boundaries.
  4. [Section 4.1, future event prediction benchmark] The future prediction test labels are produced by the same automated LLM labeling pipeline that generated WORLDREP, so the 45-61% accuracy figures in Table 5 partly measure how well one LLM predicts another LLM's labels rather than how well the models forecast real future international relationships. This is a load-bearing issue for the benchmark claim. The authors should validate a sample of the May 2024 target labels against expert or independent event ground truth, or at minimum report agreement between the labeling model and the prediction models as a caveat, and avoid presenting the raw accuracy as evidence of real-world forecasting ability.
minor comments (6)
  1. [Table 1 vs Appendix B.5.2] The dataset start date is given as February 18, 2015 in Table 1 but as February 2013 in Appendix B.5.2; these dates should be reconciled.
  2. [Section 3.1 vs Appendix B.1] The main text says 'several graduates' while Appendix B.1 says exactly three graduate students; use a single consistent description.
  3. [Figure 10] The prompt in Figure 10 lists Countries: 'CHN', 'PRK' but Step 1 asks about the relationship between 'FRA' and 'RUS', which appears to be a copy-paste error from the previous figure.
  4. [Section 4.1] The claim that the May 2024 test period is 'beyond the knowledge cutoff dates of all these models' should be substantiated per model, since some of the listed models have version-specific and sometimes later cutoff dates.
  5. [Tables 3 and 4] No confidence intervals, standard deviations, or significance tests are reported for the F1/accuracy improvements; given the small expert test set, these would help establish that the gains are not noise.
  6. [Abstract and Section 3.1] The abstract claims the labels are 'rigorously validated by domain experts' on the full dataset, but the validation is on a non-random 1,030-article subset; the wording should be tempered to match the actual scope.

Circularity Check

1 steps flagged · score 6.0 of 10

Future-event benchmark uses the paper's own automated LLM labeling output as ground truth, making the reported accuracies partly self-referential; the expert-based quality validation itself is non-circular.

  1. fitted input called prediction [Section 4, first paragraph; Section 4.1 Experimental Settings]
    "we now leverage our automated labeling system to efficiently label new data, replacing the prohibitively expensive expert labeling process. This approach allows for flexible and timely updates of WORLDREP while maintaining high data quality. Based on the results of this labeling process, we provide a benchmark using the latest large models capable of performing this task. [...] For this, we use the same categorization method described in the document classification section to convert our scoring system into categorical labels."

    The future-event benchmark defines its ground truth via the same automated LLM labeling pipeline the paper introduces, rather than independent expert labels or observed future outcomes. The evaluated models include GPT-4-Turbo and GPT-4o, the same model family used to generate the WORLDREP scores, so the reported 45-61% accuracies partly measure how well LLM predictors reproduce labels produced by the paper's own LLM labeling system, not how well they forecast independently validated international relationships. The expert validation in Section 3.1 was performed on a separate 1,030-article subset and is not shown to cover the May 2024 benchmark data, so it cannot independently certify these target labels.

full rationale

The central dataset-quality claims are externally benchmarked and non-circular: Section 3.1 compares WORLDREP labels against human expert annotations, and Section 3.2 trains models on WORLDREP versus GDELT labels and tests them on expert labels, providing an independent gold standard. The self-correcting scratchpad is also evaluated against expert labels. No load-bearing self-citation or imported uniqueness theorem appears; the author self-citations in Appendix A.4 are speculative future applications, not evidence for the main claim. However, the future-event benchmark in Section 4 replaces expert labels with the automated LLM labeling system and evaluates LLMs, including GPT-4, the labeling model family, against those self-generated labels. The paper explicitly converts its scoring system into categorical labels for this benchmark, so the Section 4 accuracy and F1 numbers partly measure self-consistency of an LLM pipeline rather than validated forecasting. This is a partial, construction-level circularity confined to the benchmark section. Separately, the headline Table 2 GDELT comparison is structurally unfair because GDELT has no Unknown label, but that is a comparison-validity issue rather than a circularity issue. The overall score reflects the independent expert validation plus the self-referential forecast benchmark.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The dataset's central quality claims rest on expert labels for a small, non-random subset and on hand-chosen thresholds and pipeline constants. The forecasting benchmark adds an ad hoc assumption that LLM-generated labels are a valid ground truth. No new entities are invented.

free parameters (5)
  • Score-to-class thresholds = 0.0-0.25 cooperation, 0.25-0.75 unknown, 0.75-1.0 conflict
    Section 3.1 maps continuous scores to three classes using these hand-chosen boundaries; the reported agreement rates in Table 2 depend on this choice, and no sensitivity analysis is given.
  • Ensemble size for label averaging = 5 runs
    Section 2.3 and Appendix B.5.4 average five scoring runs, with majority voting for unknown; the choice is not ablated.
  • Number of recent articles in prediction context = 15 articles
    Section 4.1 fixes the context to 15 recent articles before the prediction date, described as a straightforward strategy with no sensitivity experiments.
  • Keyword filter list = 50 hand-picked keywords
    Appendix B.5.2 filters articles using a hand-curated keyword list; this determines which articles enter the dataset and is not varied.
  • Summary length cap = 10 sentences, roughly 512 tokens
    Appendix B.5.3 caps summaries at 10 sentences, which affects both labeling and prediction inputs; its impact is not tested.
assumptions (6)
  • domain assumption News articles are a valid proxy for the state of international relations.
    The dataset and benchmark assume relationships described in news text reflect real inter-country relations, stated in Section 1.
  • domain assumption Domain expert annotations are an unbiased gold standard.
    Section 3.1 treats three graduate students' and one professor's labels as ground truth without reporting inter-annotator agreement.
  • ad hoc to paper LLM-generated labels for the May 2024 test period are acceptable ground truth for future event prediction.
    Section 4.1 replaces expert labeling with the automated LLM labeling system for the benchmark, an assumption not validated against experts.
  • domain assumption A single news source (CNN) provides sufficiently comprehensive coverage of international events.
    Appendix B.5.2 restricts articles to CNN to avoid duplication, limiting linguistic and editorial diversity.
  • domain assumption The self-correcting scratchpad improves label reliability.
    The method assumes the LLM's verification and correction steps yield better labels; evidence is only the F1 improvement in Table 3.
  • domain assumption GDELT's binary labels are commensurable with WORLDREP's continuous scores after thresholding.
    Section 3.1 compares the two labeling schemes by binning scores, assuming the categories align meaningfully.

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Cite this review

Pith. "Pith review of Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling." pith.science (2026). https://pith.science/paper/MM625IXY

@misc{pith2026241114042,
  author       = {Pith},
  title        = {Pith review of: Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MM625IXY}},
  note         = {Machine review of arXiv:2411.14042}
}
read the original abstract

Predicting future international events from textual information, such as news articles, has tremendous potential for applications in global policy, strategic decision-making, and geopolitics. However, existing datasets available for this task are often limited in quality, hindering the progress of related research. In this paper, we introduce WORLDREP (WORLD Relationship and Event Prediction), a novel dataset designed to address these limitations by leveraging the advanced reasoning capabilities of large-language models (LLMs). Our dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science. We showcase the quality and utility of WORLDREP for real-world event prediction tasks, demonstrating its effectiveness through extensive experiments and analysis. Furthermore, we publicly release our dataset along with the full automation source code for data collection, labeling, and benchmarking, aiming to support and advance research in text-based event prediction.

Figures

Figures reproduced from arXiv: 2411.14042 by the authors.

Figure 1
Figure 1. A two-stage annotation process using LLMs to extract key countries and analyze their rela [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. (a) Distribution of the number of key countries. Our dataset aligns more closely with domain experts compared to GDELT, which tends to identify fewer key countries. (b) Distribution of relationship labels from different sources. The proportions of conflict, cooperation, and unknown labels in our dataset closely match those of domain experts, whereas GDELT lacks the unknown cate￾gory, leading to significant imbalance… view at source ↗
Figure 3
Figure 3. Examples of self-correcting in our annotation process: (a) key country extraction and (b) [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: An LLM-based prediction framework leveraging past news articles to forecast future relationships [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Example of a questionnaire for domain experts for annotation requests. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Extracting important countries scratchpad. [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]
Figure 7
Figure 7. Figure 7: Relationship labeling scratchpad [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]
Figure 8
Figure 8. Figure 8: Example of self-correcting in extracting important countries in an article. [PITH_FULL_IMAGE:figures/full_fig_p022_8.png]
Figure 9
Figure 9. Figure 9: Example of self-correcting in labeling relationship between two countries. [PITH_FULL_IMAGE:figures/full_fig_p023_9.png]
Figure 10
Figure 10. Figure 10: Example of self-correcting in labeling relationship between two countries. [PITH_FULL_IMAGE:figures/full_fig_p024_10.png]

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Reference graph

Works this paper leans on

53 extracted references · 50 canonical work pages

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    Countries directly related to the major events or topics covered in the article

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    Countries that are major actors or play specific roles in the article. Next, we extracted major countries from the article and then asked an expert to create pos- sible country pairs from those countries and label the relationship between the two coun- tries. The guidelines provided were as follows:

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    b) Role: Countries mentioned as main actors or playing a significant role in the article

    From the extracted ‘Countries’ list, determine the important countries based on the following criteria: a) Main Event: Countries directly related to the main event or topic of the article. b) Role: Countries mentioned as main actors or playing a significant role in the article. Provide the important countries as a comma-separated list of their 3-letter co...

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    Answer ‘Yes’ or ‘No’

    Verify if all important countries are correctly identified and listed based on the content provided. Answer ‘Yes’ or ‘No’

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    Choose between ‘Conflict’ or ‘Cooperation’ to describe the relationship between coun- tries

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    Using these guidelines, the experts carried out the annotations following the questions shown in Figure 5

    If it is impossible or ambiguous to choose based on a given article, ‘Unknown’ can be selected. Using these guidelines, the experts carried out the annotations following the questions shown in Figure 5. Stage Target InstructionConsistency Country Extraction Format Instruction ...

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    This method allows for precise and comprehensive labeling of interna- tional relationships

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    Select all countries related to the content of the passage {News ID}

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    Figure 5: Example of a questionnaire for domain experts for annotation requests

    Unknown ... Figure 5: Example of a questionnaire for domain experts for annotation requests. existing works (Tan et al., 2024; Goel et al.,

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    Specifically, as shown in Figures 6 and 7, our two scratchpads have a variety of conditional instructions and detailed format guidelines for each instruction’s outcome

    that provide guidelines and a few exam- ples for a single task, our scratchpads have more questions and strict format guidelines. Specifically, as shown in Figures 6 and 7, our two scratchpads have a variety of conditional instructions and detailed format guidelines for each i...

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    A single news article or event often in- volves multiple countries with complex in- teractions

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    good" or

    Scoring Relationship Labels: Repre- senting relationships with numerical scores to capture the nuance of interactions. For instance, simply labeling relationships as "good" or "bad" can miss the subtleties of international dynamics. Numerical scores allow for a more nuanced re...

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    Handling Unknown Relationships: Identifying and labeling relationships that cannot be determined. In many cases, the information available might not be suffi- cient to ascertain the nature of the rela- tionship between countries. It is crucial to accurately label these instanc...

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    a person born in the USA

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    If the answer to step 6 is ‘Yes’, return ‘None’

    Provide the corrected summary, ensuring it includes all mentioned countries and follows the 10-sentence limit if the answer to step 6 is ‘No’. If the answer to step 6 is ‘Yes’, return ‘None’

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    If the answer to step 10 is ‘Yes’, return ‘None’

    Explain why the important countries are not accurately identified if the answer to step 10 is ‘No’. If the answer to step 10 is ‘Yes’, return ‘None’

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    If the answer to step 10 is ‘Yes’, return ‘None’

    Provide the corrected list of important countries as a comma-separated list of 3-letter country codes (ISO 3166-1 alpha-3) if the answer to step 10 is ‘No’. If the answer to step 10 is ‘Yes’, return ‘None’. Article: {text} Follow this format exactly to ensure proper parsing an...

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    If the answer to step 6 is ‘Yes’, return ‘None’.}}

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    If the answer to step 6 is ‘Yes’, return ‘None’.}}

    Corrected Summary: {{If the answer to step 6 is ‘No’, provide the corrected summary, ensuring it includes all mentioned countries and follows the 10-sentence limit. If the answer to step 6 is ‘Yes’, return ‘None’.}}

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    Important Countries: {{Answer here as a comma-separated list of 3-letter country codes based on the criteria.}}

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    Important Countries Accurate: {{Answer ‘Yes’ or ‘No’.}}

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    If the answer to step 10 is ‘Yes’, return ‘None’.}}

    Explanation for Inaccuracy: {{If the answer to step 10 is ‘No’, explain why the important countries are not accurately identified. If the answer to step 10 is ‘Yes’, return ‘None’.}}

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    You are provided with an article

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  27. [52]

    If no countries are mentioned, return "None"

    Extract all countries mentioned in the article as a comma-separated list of their 3-letter country codes (ISO 3166-1 alpha-3). If no countries are mentioned, return "None"

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    Summarize the article in up to 10 sentences, ensuring all mentioned countries are included

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    Answer ‘Yes’ or ‘No’

    Verify if the extracted ‘Countries’ list includes all countries mentioned in the article. Answer ‘Yes’ or ‘No’

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    Explain why the ‘Countries’ list is not accurate if the answer to step 3 is ‘No’. If the answer to step 3 is ‘Yes’, return ‘None’

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    If the answer to step 3 is ‘Yes’, return ‘None’

    Provide the corrected list of countries as a comma-separated list of 3-letter country codes (ISO 3166-1 alpha-3) if the answer to step 3 is ‘No’. If the answer to step 3 is ‘Yes’, return ‘None’

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    Answer ‘Yes’ or ‘No’

    Verify if the ‘Summary’ of the article content is accurate and includes all mentioned countries. Answer ‘Yes’ or ‘No’

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    If the answer to step 6 is ‘Yes’, return ‘None’

    Explain why the ‘Summary’ is not accurate if the answer to step 6 is ‘No’. If the answer to step 6 is ‘Yes’, return ‘None’. Article: {text} Countries: {country1}, {country2} Follow this format exactly to ensure proper parsing and then answer:

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    Countries: {{Answer here as a comma-separated list of 3-letter country codes, or "None".}}

  35. [60]

    Ensure you include as much information from the original article as possible.}}

    Summary: {{Answer here with the summary text including all mentioned countries, up to 10 sentences. Ensure you include as much information from the original article as possible.}}

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    Countries Accurate: {{Answer ‘Yes’ or ‘No’.}}

  37. [62]

    If the answer to step 3 is ‘Yes’, return ‘None’.}}

    Explanation for Inaccuracy: {{If the answer to step 3 is ‘No’, explain why the ‘Countries’ list is not accurate. If the answer to step 3 is ‘Yes’, return ‘None’.}}

  38. [63]

    If the answer to step 3 is ‘Yes’, return ‘None’.}}

    Corrected Countries: {{If the answer to step 3 is ‘No’, provide the corrected list of countries as a comma-separated list of 3-letter country codes (ISO 3166-1 alpha-3). If the answer to step 3 is ‘Yes’, return ‘None’.}}

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    Summary Accurate: {{Answer ‘Yes’ or ‘No’.}}

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    Malligyong-1

    Explanation for Inaccuracy: {{If the answer to step 6 is ‘No’, explain why the ‘Summary’ is not accurate. If the answer to step 6 is ‘Yes’, return ‘None’.}} Figure 7: Relationship labeling scratchpad. Article: North Korea announced on Wednesday that it had launched its first r...

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.