HouseOfTheDragonQA: Open-Domain Long-form Context-Aware QA Pairs for TV Series
Bibliographic record
Abstract
This paper proposes a novel approach to develop an open-domain and long-form Over-The-Top (OTT) Question-Answering (QA) dataset, HouseOfTheDragonQA, specifically oriented to the “House of the Dragon” TV series. Most of the existing QA datasets have focused on short, fact-based answers sourced almost solely from Wikipedia articles-bereft of the depth and contextual richness required for sophisticated narrative understanding. Our dataset is curated using legally admissible and high-quality open-domain sources to combine full episode summaries from HBO and fandom wiki websites, user reviews from IMDb and Rotten Tomatoes, and structured data from repositories such as WikiData. The dataset provides a multidimensional context, capturing complex character dynamics and plot developments from these varied sources. On equal terms, rigorous data preprocessing and filtering methods ensure that only meaningful and non-spam, unbiased reviews will be present in this enhanced dataset. The long-form answers generated from this enriched context provide comprehensive insights, making this dataset particularly valuable for improving conversational AI, narrative analysis, sentiment analysis, summarization techniques, and relation extraction. Comparative analysis with state-of-the-art QA datasets like SQuAD 2.0, TriviaQA, and Natural Questions (NQ) demonstrates the unique advantages of our dataset in terms of contextual complexity and answer length. The inclusion of detailed reviews offers added layers of audience sentiment and narrative interpretation, setting a new benchmark for quality in domain-specific QA tasks. Our effort enables advanced comprehension of entertainment-industry content and paves the way for more knowing and creative AI-driven interactions within digital media settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".