Dataset opportunity
Tolid — LLM-annotated corpora: crypto news, biomedical patents, beauty video
A data company selling the corpora it built — and still operating the pipeline that built them.
Score
84
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
60%
Action
License
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market
Annotated training data whose label quality is MEASURED, not asserted — a claim almost no competing corpus can make. Sold with its defects declared, and with the pipeline that produced it available as a service.
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Profile
Dataset profile
Type
LLM-annotated corpora / knowledge graphs
Modality
text
Sector
AI training data
Volume
2.47M docs · 723k patents · 449 routines
Freshness
Archive — pipelines restartable
Rarity
High — the annotation, not the text
Accessibility
Immediate — BigQuery / Firestore mirrors
Legal
Annotations, graphs and metadata are derived works produced by Tolid and are transferable. The source press text is NOT (it belongs to the publishers) — it is excluded from every delivery. BIOPORTAL — measured and settled (2026-07-14): 707 ontologies are used, not one. The 13,757 rows (1.6% of the mapping table) coming from restrictively licensed sources — SNOMED CT, MedDRA, RxNorm, OMIM, NDDF, Read Codes, ICD, CPT — are EXCLUDED from every delivery. Everything else is open by construction (OBO Foundry mandates CC-BY/CC0 of its members; MeSH is free for commercial use). CC-BY is an ATTRIBUTION obligation, not a copyleft: an attribution NOTICE ships with the data. STILL OPEN: the Open Beauty Facts ODbL status (beauty corpus) — adversarial review refuted the 'Produced Work' reading, so the constraint may prove BINARY rather than a price discount. The four biometric inference columns (perceived sex, age range, emotion, skin tone) are excluded from any delivery.
Buyer persona
Publishers of domain LLMs and financial-NLP models · pharma and biotech R&D (patent intelligence) · cosmetics groups and beauty-tech · data vendors who want to train their own classifier. NOT hedge funds or signal vendors for the crypto corpus — it carries no alpha, and we say so.
Tolid is a data company. It does not resell someone else's exhaust: it built the collection and LLM-annotation pipelines that produced these corpora, and it still operates them. What is on offer is therefore two things at once — a stock of annotated data, and the machine that produced it.
Three corpora are on the table.
1. Crypto news, annotated (2,469,218 documents, Oct 2023 → Sep 2025). Sentiment, topic, summary and a buy/hold/sell tag, on normalised tickers. Read this before anything else: the trading signal has NO measured predictive power. We ran the backtest against real prices ourselves, on all 1,198,479 signals: it beats a coin flip by 0.7 points, and its market-neutral alpha is nil. The reason is in the data — press sentiment correlates at +0.60 with PAST returns and +0.07 with future ones. It follows the price; it does not lead it. We are not selling alpha, and this corpus is not for a trading desk. What that same +0.60 does prove is that the annotation is faithful: it is an LLM-labelled financial corpus whose label quality has been validated against an external ground truth. That is what you are buying — training data with a measured quality, not a signal.
2. Biomedical patents, structured as a knowledge graph (723,149 documents). The most valuable asset of the three, and the one carrying the largest open question: the licensing terms of the BioPortal ontologies it aligns to are under review. We say so before you ask.
3. Procedural beauty video (449 routines, 7,656 timestamped gestures, 21,441 videos). Not "video data": an analytical surface over what people actually do, step by step, with which products. Two constraints declared up front — the Open Beauty Facts ODbL question is unresolved and may be binary rather than a discount, and the four biometric inference columns (perceived sex, age, emotion, skin tone) are excluded from any delivery. The procedural graph keeps all of its value without them.
What we do not sell: the source article text. It belongs to the publishers, not to Tolid. What is transferable is the derived work — the annotations, the URLs, the metadata, the graph.
Services
What the holder can also do for you
This dataset is not only a stock — its holder still operates the pipeline that produced it. Each capability below is backed by an observed fact, not a claim.
Human validation of the annotations, on demand
Tolid can run a human review pass over any corpus it has produced — full or sampled, to your own guidelines and quality bar.
Evidence — The entire human-in-the-loop layer already exists — nine moderation tables, an escalation queue, prompt structures — and has never been run on a single document (0 of 4,841,938). The machine is built and wired; it has simply never been switched on.
A corpus built to your theme, from scratch
Give a domain and a taxonomy; Tolid runs collection, LLM annotation and graph construction end-to-end — the same pipeline that produced these corpora.
Evidence — The pipeline is theme-driven, and there is proof: a seventh theme ("Geopolitical Tensions and Alliances") is fully configured — prompt and JSON template written — with no collection and no deployed service. A theme conceived, written, never launched. That is what "give us a subject" looks like in this codebase.
Entity resolution and normalisation
Mapping the messy surface forms of a domain onto canonical entities — the unglamorous work that decides whether a corpus is joinable with your own data.
Evidence — 21,359 alias → canonical-ticker mappings were built for the crypto corpus; 19,080 semantic mappings for the beauty ontology. This is the domain's price of entry, already paid.
Cleaning and extending an existing signal
Re-parsing, de-duplicating and extending a field that was produced once and never curated.
Evidence — Declared openly: the buy/hold/sell field covers only ~44% of the crypto rows and carries parsing leaks (`buy (implied)`, `hold/sell`). Tolid can clean and extend it. We would rather sell you the fix than hide the defect.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
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See dimension details ↓- Dataset Specificity88
Annotation depth, not raw text: sentiment, topic, summary, triples — on 2.5M documents, plus a 723k-patent knowledge graph.
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity88
The text is abundant; the annotation at this depth and history is not. Engine median across three runs: 85–90.
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume90
2,469,218 annotated crypto documents · 723,149 patent documents · 449 procedural beauty routines with 7,656 timestamped gestures.
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Training Value85
Built for model training, not for reporting — and the label quality is measured, not asserted (see the crypto validity study).
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand87
Engine median 85–90 once demand is weighted by buyer solvency rather than by the number of names.
How strongly AI builders and companies are likely to want this data, based on market signals. - Evidence Strength45
Deliberately low: the price range comes from our valuation engine, and NO external market comparable anchors it yet. Two deep-research studies are pending. We would rather show a weak score than a confident number we cannot defend.
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Data Orientation95
A data company: the pipelines, the graph and the annotation layer are the product, not a by-product.
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
Produced, stored — and never monetised. The moderation layer has never run; the crypto corpus has been frozen since 2025-09-20.
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
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Marketplace
Dataset details
Geographic coverage
Global (multilingual sources)
Time range
2023-10 → 2025-09 (crypto) · patents and beauty: see each dataset
Delivery
Secure extract download, or scoped access
Formats
parquet, csv, jsonl
License
Derived works (annotations, graphs, metadata) are transferable. Source press text is not.
Personal data
No PII
There is no public price grid for these corpora, and we will not manufacture one. Our own valuation engine returns wide, UNANCHORED ranges — dispersion up to ×7 on the crypto asset across three identical runs — and a dedicated deep-research study (July 2026, 103 agents, 86 claims → 25 adversarially verified) confirmed WHY: for an LLM-annotated news archive sold as annotations-only, no defensible market comparable exists. The published editor↔LLM deals disclose amounts but never volumes, so no per-document price can be derived; and the closest academic comparable (Financial PhraseBank) anchors label PEDIGREE, never a number. The gap is in the market, not in our research. So we price ON REQUEST, against two things we can defend: a measured production cost (from real cloud billing) and a label quality validated against external ground truth. The biomedical-patents asset — the strongest of the three — is under its own valuation study; its price will be shown only once anchored on real dataset transactions, never on a SaaS-subscription proxy. You do not buy the bundle: each dataset is priced on its own.
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Deal room
Tolid — LLM-annotated corpora: crypto news, biomedical patents, beauty video
There is no public price grid for these corpora, and we will not manufacture one. Our own valuation engine returns wide, UNANCHORED ranges — dispersion up to ×7 on the crypto asset across three identical runs — and a dedicated deep-research study (July 2026, 103 agents, 86 claims → 25 adversarially verified) confirmed WHY: for an LLM-annotated news archive sold as annotations-only, no defensible market comparable exists. The published editor↔LLM deals disclose amounts but never volumes, so no per-document price can be derived; and the closest academic comparable (Financial PhraseBank) anchors label PEDIGREE, never a number. The gap is in the market, not in our research. So we price ON REQUEST, against two things we can defend: a measured production cost (from real cloud billing) and a label quality validated against external ground truth. The biomedical-patents asset — the strongest of the three — is under its own valuation study; its price will be shown only once anchored on real dataset transactions, never on a SaaS-subscription proxy. You do not buy the bundle: each dataset is priced on its own.
Buyer persona
Publishers of domain LLMs and financial-NLP models · pharma and biotech R&D (patent intelligence) · cosmetics groups and beauty-tech · data vendors who want to train their own classifier. NOT hedge funds or signal vendors for the crypto corpus — it carries no alpha, and we say so.
The type of company or team most likely to buy or use this dataset — the target on the demand side.Market
Annotated training data whose label quality is MEASURED, not asserted — a claim almost no competing corpus can make. Sold with its defects declared, and with the pipeline that produced it available as a service.
A rough read on demand and price band for this data, from market signals ($ = niche, $$$ = high AI-buyer demand).Risk
Annotations, graphs and metadata are derived works produced by Tolid and are transferable. The source press text is NOT (it belongs to the publishers) — it is excluded from every delivery. BIOPORTAL — measured and settled (2026-07-14): 707 ontologies are used, not one. The 13,757 rows (1.6% of the mapping table) coming from restrictively licensed sources — SNOMED CT, MedDRA, RxNorm, OMIM, NDDF, Read Codes, ICD, CPT — are EXCLUDED from every delivery. Everything else is open by construction (OBO Foundry mandates CC-BY/CC0 of its members; MeSH is free for commercial use). CC-BY is an ATTRIBUTION obligation, not a copyleft: an attribution NOTICE ships with the data. STILL OPEN: the Open Beauty Facts ODbL status (beauty corpus) — adversarial review refuted the 'Produced Work' reading, so the constraint may prove BINARY rather than a price discount. The four biometric inference columns (perceived sex, age range, emotion, skin tone) are excluded from any delivery.
The main legal and compliance constraints on using or transferring this data — PII/GDPR, licensing rights, regulatory limits.Action
License
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Coverage
Scanned sources
Deliverable
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