Definitive, Structured, Quotable: The 3-Part Test Every Video Must Now Pass

The Checklist Neil Patel Handed the Whole Industry — and How to Run Any Script, Interview, or Corporate Film Through It Before You Shoot

There is now a single pass/fail test for whether a video earns an AI citation, and Neil Patel stated it in three words: content gets cited when it is definitive, structured, and quotable. His one-line version is the whole standard — an AI system should be able to lift one sentence out of your content and have it stand on its own.

The data behind each leg:

• AI engines extract passages, not pages. They chunk a page, score each passage independently, and lift the single strongest one. The unit of citation is the sentence, not the article. (Kime.ai)
• 40–75-word passages were cited 3.1x more often than longer passages, and 2.4x more than shorter ones (2025 analysis of 10,000 AI citations, cited in Kime.ai). A 15-second spoken soundbite runs ~40–45 words. The quotable leg has a literal word count.
• 44.2% of all LLM citations come from the first 30% of the text. (Quattr) Front-load the answer or forfeit the citation. That is the structured leg.
• The winning structure is “Question, Answer, Evidence” — an H2 phrased as the question, a 1–2 sentence direct answer, then the proof. (Quattr) That is the definitive leg, formatted.
• Videos with complete VideoObject schema show 40–60% higher inclusion rates in AI-generated responses than videos with minimal or absent structured data. (Swarmify / ALM Corp) The same three-part test applies to the markup, not just the words.

The reframe: most corporate video is shot to be watched and judged on whether it looks good. In 2026 it has to be built to be lifted — one sentence at a time, by a machine, to answer a buyer’s question. This post turns Patel’s three-word test into a checklist a marketing lead can run on any script before the camera rolls, and on any finished film before it ships. Written for the corporate B2B buyer and the in-house marketer who has to defend the spend.

1. The Test, Stated Plainly: Definitive, Structured, Quotable

Start with the standard itself, because it is unusually clean for marketing advice. Neil Patel’s framing for what content AI engines cite is three properties: it has to be definitive (the most authoritative answer to a specific question), structured (organized so a machine can find the answer), and quotable — and his test for “quotable” is the sharpest line in the whole discipline: an AI system should be able to lift one sentence out of your content and have it stand on its own.

That is not a vibe. It is a pass/fail. Take any sentence from your last video’s transcript, paste it into a document with no surrounding context, and read it cold. If it still makes complete sense, names its subject, and states something specific, it passes. If it reads as a fragment that needs the three sentences before it to mean anything, it fails — and the engine will skip it.

Here is why all three legs are really one test wearing three hats:

• Definitive answers the question which sentence the machine wants — the authoritative one, not the hedged one.
• Structured answers where the machine finds it — front-loaded, under a clear heading, in a Question-Answer-Evidence shape.
• Quotable answers whether the sentence survives extraction — self-contained, declarative, attributable, the right length.

Run those three checks on a sentence and you have predicted, with surprising accuracy, whether an AI will cite it. The rest of this post is the operational version of those three checks — what they mean for a script, an interview, and a finished film.

2. The Mechanic That Makes the Test Real: Passages, Not Pages

Patel’s test only matters because of one mechanical fact about how AI engines read, and it is the fact most video buyers have never been told: AI does not read your page the way a human does. It chunks it. It breaks the page into individual passages, scores each passage on its own merits, and lifts the single strongest one to answer the user’s question. As one extraction guide put it: “AI models do not read pages. They extract passages.” (Kime.ai)

The retrieval pipeline behind that — retrieval-augmented generation — stores your content as semantic vectors, matches the user’s question against them, and pulls back the chunk whose meaning best fits. It is not scanning for keywords. It is scoring passages for relevance and information density and pulling the densest match. (Discovered Labs)

Two consequences fall straight out of this, and both rewrite how a video should be built:

1. The unit of citation is the sentence, not the film. Your beautifully arced 8-minute brand story does not get cited as an 8-minute brand story. One sentence from its transcript gets cited — or none do. The narrative flow that makes humans feel something is invisible to the chunker; only the standalone strength of each passage is visible.
2. Length is a passage property, not a page property. The 2025 analysis of 10,000 AI citations found 40–75-word passages were cited 3.1x more often than longer ones, and 2.4x more often than shorter ones. A spoken soundbite of about 15 seconds runs roughly 40–45 words. The “quotable” leg of Patel’s test has a measurable target, and it is the size of a broadcast soundbite — which is the whole argument of “The Unrepeatable Sentence.”

So when Patel says “an AI should be able to lift one sentence and have it stand on its own,” he is describing the literal operation the engine performs. The test isn’t a metaphor. It’s the spec.

3. Leg One — DEFINITIVE: One Question, One Authoritative Answer

The first leg is the one most marketers get wrong, because “definitive” sounds like “comprehensive” and it is nearly the opposite. Definitive does not mean cover everything. It means: for one specific question, deliver the single most authoritative answer, in a sentence that commits.

Three things make a passage read as definitive to an engine:

It answers a specific question, not a topic. “Safety on jobsites” is a topic; no sentence can be the definitive answer to a topic. “What is the most common cause of a failed holdback inspection?” is a question, and exactly one sentence can be its best answer. The Princeton GEO study (Aggarwal et al., peer-reviewed at ACM KDD 2024) found that the content additions which most boost AI visibility are Cite Sources, Quotation Addition, and Statistics Addition — improving visibility 30–40%. (GEO: Generative Engine Optimization) All three are ways of making a specific claim defensible — and defensible is what definitive means in practice.

It is declarative, not hedged. GEO writing data shows authoritative, declarative prose lifted citation rates 25% over tentative phrasing. “Structured data reduces entity ambiguity” beats “structured data may help reduce entity ambiguity.” The cautious, qualified, legal-reviewed corporate voice — the one that adds “in many cases” and “can sometimes” to every sentence — is structurally anti-citable. Conviction is a ranking factor.

It commits to a number when one exists. Statistics get 40% higher citation rates than qualitative statements (Onely). “We cut rework significantly” is not definitive. “We cut rework by 18% on that project” is. The number is what makes the sentence the authoritative answer rather than one opinion among many.

For a video, “definitive” is a question-design problem before it is an editing problem. If your interview question list is a sequence of vague prompts (“tell us about your company”), you will get vague, topic-level answers that no engine can cite. If it is a list of specific questions each with one authoritative answer — which is exactly how the Art of Documentary method builds a question list, as plot points rather than a script — you get definitive passages by design. The interview question list and the GEO content brief are the same document.

4. Leg Two — STRUCTURED: Front-Load the Answer, Then Prove It

The second leg is where most of the citation is won or lost, and it comes down to a single number: 44.2% of all LLM citations come from the first 30% of the text. (Quattr) Nearly half of all citations are pulled from the opening. Bury your best answer in minute six of the film, or paragraph nine of the transcript, and you have hidden it from the part of the page the engine cites most.

The pattern that wins is explicit, and Quattr names it: Question, Answer, Evidence.

Element	What it is	Why the engine rewards it
Question	An H2 (or a video chapter title) phrased as the actual question a buyer asks	Matches the user’s query directly — the semantic-relevance signal
Answer	A 1–2 sentence direct answer, immediately, no throat-clearing	This is the 40–75-word chunk the engine lifts; front-loaded into the first 30%
Evidence	The statistic, the named example, the cited source beneath it	The Princeton “cite sources + statistics” levers; makes the answer defensible

This is the structure of a good FAQ, and it is also — not coincidentally — the structure of a well-built video chapter. A chapter titled “How do you keep a pour on schedule in winter?” that opens with the foreman saying, in one clean sentence, exactly how, followed by the specific example, is a Question-Answer-Evidence block in video form. It front-loads the answer into the chapter, which is itself a citation surface.

And the structuring extends past the words into the file. The OtterlyAI YouTube Citation Study 2026 (100M+ AI citations analyzed) found 31% of cited videos contained timestamp signals, and 78% of timestamped videos were cited multiple times — often across several chapters. Chapters are not a convenience feature. They are how you turn one video into several front-loaded, separately-extractable answers. A 12-minute interview with eight clean chapters is eight Question-Answer-Evidence blocks, each with its own “first 30%.”

The structured leg, in one rule: the answer goes first, the proof goes second, and the file is chaptered so every answer gets its own front.

5. Leg Three — QUOTABLE: The Sentence Has to Survive Extraction

The third leg is Patel’s headline test, and it has four properties that every credible source converges on. A quotable sentence is self-contained, declarative, specific, and attributable.

• Self-contained — it makes complete sense ripped out of context. The extractability test, from Omnia: “Could this paragraph be pulled out of the article and still make complete sense?” If it depends on the sentence above it, the engine passes over it.
• Declarative — it states a fact directly. (The 25% citation lift from declarative over hedged phrasing.)
• Specific — concrete, with a name or a number, not a generality.
• Attributable — it names its subject explicitly. Replace “we” and “it” with the company, the role, the project — so the lifted sentence carries its own attribution.

Here is the thing video does that written content cannot fake: a good interview produces quotable sentences as its primary output. A founder explaining “why we never skip the holdback inspection” in one clean, complete sentence on camera is the self-contained, declarative, specific, attributable chunk — and it now exists in three forms at once: as spoken audio, as an on-screen caption, and as a transcript line.

The broadcast craft of producing a soundbite — brief, complete, says-it-in-one-line — is the craft of producing a quotable chunk. They are the same artifact at the same word count.

This is why the “quotable” test is really a capture problem, not a writing problem. A blog writer has to phone an expert and hope for a usable line. A documentary interviewer engineers the line in the room — coaching the subject to fold the question into the answer, to name the entity, to say it as “one sentence I could put on screen.”

That is exactly the Library Remix discipline: capture a deep, well-structured interview once, then harvest dozens of standalone, quotable passages from it. We make the whole case for interview-as-citation-engine in “The Unrepeatable Sentence.”

6. The 3-Part Test as a Checklist You Can Run Before You Shoot

Here is the practical payoff: the three-word test becomes a checklist a marketing lead can run on a script before the crew arrives, and on a finished film before it ships. Print this. Run it on your next video.

Stage 1 — Run it on the question list (before the shoot):

• Definitive: Is each question a specific question, not a topic? Could exactly one authoritative sentence answer it? (If the question is “tell us about your work,” rewrite it until it has a single best answer.)
• Definitive: Have you planted at least one “what’s the number?” question and one “define the term” question? (Statistics and definitions are disproportionately cited.)
• Structured: Have you ordered the questions so the most important answer can live in the first chapter? (Front-load the 30% that gets 44.2% of citations.)
• Quotable: Is there a “say that as one sentence I could put on screen” prompt attached to each key question? (This produces the standalone chunk on demand.)
• Quotable: Will you coach the subject to name the company/role/project rather than say “we”? (Attribution is built in the room, not in post.)

Stage 2 — Run it on the finished film (before it ships):

• Quotable: Pick five sentences from the transcript at random. Paste each into a blank doc and read it cold. Do at least four of five stand alone, name a subject, and state something specific? (If not, the citation surface is thin — recut or pull-quote.)
• Structured: Is the strongest answer in the first chapter, or buried in minute six? (Move it, or add a chapter that front-loads it.)
• Structured: Does the video ship with a human-reviewed transcript, chapters, and timestamps? (Auto-captions are not enough — transcript errors become misquotes in the AI summary.)
• Structured: Is the published page built Question-Answer-Evidence — H2 as the question, the answer first, the proof beneath?
• Definitive + Quotable: Is the single best soundbite pulled out as an attributed on-page quote near the top — “‘[Sentence],’ said [Name], [Title] at [Company]”?
• Structured (machine layer): Is VideoObject schema applied, with the transcript, chapters/Clip, and uploadDate? (40–60% higher inclusion in AI responses.)

A film that clears that checklist has passed the definitive/structured/quotable test at the word level, the file level, and the markup level. A film that fails it can be gorgeous, expensive, and completely uncitable.

7. The Library Remix: One Shoot Day, Fifty Passages That Pass

The three-part test scales badly if you think one video equals one citable thing. It scales beautifully under the Library Remix Model — the principle that one deep, well-structured interview is not a single asset but a library of standalone passages you harvest over time.

Run the math against the test. A 12-minute interview built on the checklist above produces roughly 1,500–2,000 words of transcript. Inside that transcript, if the questions were designed for definitive answers, you have eight to twelve sentences that each pass the quotable test on their own — each one a 40–75-word chunk that stands alone, names its subject, and states something specific. Chapter the video around those answers and, per the OtterlyAI finding, each chapter becomes a separately-citable passage (78% of timestamped videos were cited multiple times).

So one shoot day yields, at the passage level:

• 8–12 standalone, quotable transcript sentences — each a candidate citation chunk
• 6–10 video chapters — each a front-loaded Question-Answer-Evidence block and its own citation surface
• The same passages re-expressed as on-page pull-quotes, as LinkedIn clips, as blog answers, as podcast segments

Each of those is a distinct place the same definitive answer can be lifted from. The Library Remix is what makes the three-part test economically sane: you don’t pass the test once per video — you pass it dozens of times from one capture, because the capture was a library of passages, not a single linear film. We detail the one-capture-to-many-surfaces mechanics in “One Shoot Day, a Year of Citation Surfaces.”

This is also why “we already have a brand video” rarely passes. A single linear brand film, shot to be watched start to finish, usually contains zero sentences engineered to stand alone — because it was built for narrative flow, not extraction. The Library Remix builds for both: a film that moves a human and a transcript full of passages that move a machine.

8. Why Video Passes the Test More Reliably Than Written Content

A fair question from any marketer: if the test is about sentences, why not just write better sentences? Why shoot video at all?

Because video clears two legs of the test that written content struggles with, and it clears them by default.

It produces the quotable sentence under pressure. Writing a self-contained, declarative, attributable sentence is easy to describe and hard to do at volume — most corporate copy collapses into hedged, committee-softened mush. An interview, run with real technique, manufactures quotable sentences as its natural output because a person speaking aloud about something they actually did tends to say it in one committed line. The craft of the interviewer is to capture and sharpen that line — the E/I question tagging, the WHAT-questions, the circle-back, the never-say-“be comfortable” rule that turn a rambling answer into a soundbite.

It carries the first-hand Experience signal that text usually can’t prove. The strongest authority signal in 2026 is demonstrated first-hand experience — and an interview is captured first-hand experience by definition. The named superintendent describing the incident he lived through is delivering Experience the engine can verify came from a human who was there. A text page asserting the same thing has no such proof. We make that whole case in “First-Hand Experience Is the New #1 Ranking Signal.”

And it fans out into more passages per dollar. A blog post atomizes into text. A video atomizes into text and video and audio and social clips — every one a place the same definitive answer can be lifted. Per the Library Remix, one capture clears the test across four surface types, not one.

The honest version: you can pass the three-part test with pure writing, and you should apply it to every page you publish. But video passes it more reliably, at higher volume, with a built-in trust signal text can’t replicate — which is why the test reframes video from “decoration you watch” to “the most efficient passage factory you own.”

9. The 5 Counter-Intuitive Findings

1. Comprehensive loses; definitive wins. “Definitive” is nearly the opposite of “cover everything.” A tight, committed, specific answer to one question out-cites a sprawling overview, because the engine lifts one passage and a sprawling overview has no single strongest one. The instinct to be thorough produces uncitable mush.
2. The first 30% does almost half the work. 44.2% of LLM citations come from the opening of the text (Quattr). Where you put the answer matters as much as the answer. Burying your best line in minute six is hiding it from the part of the page that gets cited most.
3. The soundbite-sized chunk beats the long passage 3.1x. 40–75-word passages were cited 3.1x more often than longer ones. More words is not more authority. The broadcast instinct (be brief and complete) beats the SEO instinct (be exhaustive).
4. Hedged, “balanced” writing is penalized. Declarative prose lifts citation 25% over tentative phrasing; keyword stuffing scored 10% below baseline (Princeton). The cautious corporate-comms voice — the one legal softened — is structurally anti-citable. Conviction is the asset.
5. The schema is part of the test, not an afterthought. Videos with complete VideoObject markup show 40–60% higher inclusion in AI responses (Swarmify). A film can pass the test at the word level and still fail it at the file level if it ships as a bare MP4 with no transcript, chapters, or markup.

10. FAQ

What is the 3-part test for a video to be cited by AI?

A video (or any content) earns AI citation when it is definitive, structured, and quotable — Neil Patel’s three-word standard. Definitive means it gives the single most authoritative answer to a specific question, in declarative, committed language. Structured means the answer is front-loaded and organized Question-Answer-Evidence, with chapters and a transcript. Quotable means the test Patel states directly: an AI should be able to lift one sentence out and have it stand on its own — self-contained, specific, and attributable.

What does “quotable” actually mean in measurable terms?

A quotable sentence is self-contained (makes sense ripped out of context), declarative (states a fact directly), specific (carries a name or a number), and attributable (names its subject explicitly). It also has a length: the 2025 analysis of 10,000 AI citations found 40–75-word passages were cited 3.1x more often than longer ones. A 15-second spoken soundbite runs ~40–45 words — almost exactly that target. The test: paste a sentence into a blank document, read it cold, and see if it still makes complete sense.

Why does “definitive” not mean “comprehensive”?

Because AI engines lift one passage, not a whole page. A comprehensive overview has no single strongest sentence — its authority is spread across paragraphs the chunker reads independently and scores as mediocre. A definitive answer concentrates authority into one committed, specific sentence the engine can lift whole. Trying to cover everything dilutes exactly the thing that gets cited. Answer one specific question authoritatively, then move to the next.

Where should the most important answer go in a video?

In the first chapter, and on the published page, in the first 30% of the text — because 44.2% of all LLM citations come from there. The winning structure is Question-Answer-Evidence: the chapter title (or H2) is the buyer’s actual question, the opening line is the direct answer, and the proof follows. Burying your best answer deep in a long film hides it from the part of the page the engine cites most. Front-load it.

Can I make my existing brand video pass this test?

Partly. You can add a human-reviewed transcript, cut chapters around the strongest answers, pull the best soundbite out as an attributed on-page quote, and apply VideoObject schema — all of which raise its citability. What you usually can’t retrofit is the capture: a brand film shot for narrative flow rarely contains sentences engineered to stand alone, because no one in the room was coached to produce them. The reliable path is to build the next shoot on the checklist — definitive question list, quotable-sentence coaching, chaptered delivery.

Does this only matter for marketing videos, or for any video?

Any video meant to demonstrate expertise or win a buyer’s trust — executive interviews, customer-story films, project case studies, recruiting and culture content, founder explainers. All of them are top-of-funnel authority content, which is exactly what AI engines cite. A purely transactional or internal video doesn’t need to pass the test. But anything you’d want surfaced when a buyer asks an AI “who does credible work in [your category]?” has to clear definitive, structured, and quotable — or it stays invisible.

How is this different from old-school video SEO?

Old video SEO optimized titles, tags, and thumbnails to rank a video in Google’s video carousel. The 3-part test is about being cited as the recommended answer inside an AI engine — ChatGPT, Perplexity, Gemini, Google AI Overviews — which pull from transcripts and passages, not view counts or keyword-stuffed tags. You’re no longer optimizing a video to be clicked. You’re engineering its transcript so a machine can lift one sentence and repeat it as the answer.

11. The Take-Home

For two decades, the brief for a corporate video was “make it look good and make people watch.” That brief is now incomplete to the point of being dangerous, because the most important viewer your video will ever have doesn’t watch at all. It reads the transcript, chunks it into passages, scores each one alone, and lifts the single strongest sentence to answer a buyer’s question — or lifts nothing, if no sentence stands on its own.

Neil Patel compressed the new brief into three words: definitive, structured, quotable. Give the single most authoritative answer to a specific question. Front-load it and organize it Question-Answer-Evidence, with chapters and a clean transcript. And make every key sentence one an AI could lift out and have stand on its own — self-contained, declarative, specific, attributable, soundbite-sized.

That is not a creative constraint. It is a checklist, and you can run it before the camera rolls. A film that clears it gets cited dozens of times from one shoot, because a well-built interview is a library of passages that pass. A film that fails it can be the most beautiful thing you produce all year and still never get repeated by the machine your buyer now asks first.

Most companies still shoot video to be watched. The work now is to shoot it to be lifted — one definitive, structured, quotable sentence at a time.

12. About the Author

Jared Ho is the founder of Storimatic Studio (Calgary video production), the founder of Biostack (AI-visibility / GEO-AEO agency), and the owner of the Omega Group of companies (Omega Ready Mix · Omega 2000 Cribbing · Omega Precast — Edmonton). He has run hundreds of interviews using the Art of Documentary method, and he applies the definitive/structured/quotable test to his own footage before it ships — because he also runs the agency that measures whether the machine cites it.

The double credential is the point: as a construction operator he knows the definitive answers live inside his foremen and PMs, and as an AI-visibility strategist he knows the difference between a sentence the machine lifts and a sentence it skips. He watched a five-person Edmonton precast manufacturer go from invisible to top-3-cited in Alberta AI search by building exactly this kind of liftable, structured proof.

13. Book a Discovery Call

If you want to know whether your current videos pass the test — whether there’s a single sentence in your footage an AI could lift and repeat — book a 30-minute discovery call. We’ll run your category’s top buyer questions through ChatGPT, Perplexity, and Gemini, audit your existing video for definitive/structured/quotable passages, and map the 5–7 interview questions most likely to produce citable answers for your category.

Book a discovery call with Storimatic Studio today. We don’t quote a production without that conversation. The footage was never the product. The liftable sentence is.

Sources

Primary anchor data:

• Quattr — How to Get Cited by LLMs (44.2% from first 30%; Question-Answer-Evidence)
• Kime.ai — How to Structure Content for LLM Extraction (passages not pages; 40–75 words 3.1x)
• Princeton GEO study (Aggarwal et al., ACM KDD 2024): quotations + statistics + cited sources lift 30–40% · summary
• OtterlyAI — YouTube Citation Study 2026 (78% of timestamped videos cited multiple times)
• Swarmify — Video Schema Markup (VideoObject 40–60% inclusion lift)

Mechanism + extractability:

• Discovered Labs — Technical Mechanics of LLM Content Retrieval
• Omnia — AI Content Extractability (the “pull it out and does it still make sense” test)
• Onely — LLM-Friendly Content (statistics +40% citation)
• Neil Patel — Search Everywhere Optimization

Last updated: May 2026 | Methodology: Patel’s definitive/structured/quotable framing; Quattr extractability data; Kime.ai passage-length analysis (10,000 citations); Princeton GEO study (KDD 2024); OtterlyAI 100M+ citation study; Swarmify VideoObject analysis; synthesized with the Storimatic AOD interview method and Library Remix Model. Re-verify the percentage magnitudes quarterly; the underlying mechanic (passages, not pages; the three-leg test) is durable.

GEO/AEO Schema Markup Notes (for publisher)

• Article schema — author = Jared Ho (Person), publisher = Storimatic Studio, datePublished = “2026-05-20”, mentions = [Neil Patel, Quattr, Kime.ai, Princeton, OtterlyAI, Swarmify]
• FAQPage schema — wrap Section 10
• VideoObject schema — any embedded clip gets full markup (transcript, Clip/chapters, uploadDate, description) — this post about the test should itself pass the test
• HowTo schema — Section 6 (the pre-shoot/pre-ship checklist) marked up as a HowTo with steps
• DefinedTerm schema — “definitive” · “structured” · “quotable” · “passage extraction” · “Question-Answer-Evidence” · “Library Remix” · “citable chunk” · “VideoObject schema”
• Statistic / Claim schema — 3.1x, 2.4x, 44.2%, 30–40%, 40%, 25%, 10%, 78%, 31%, 40–60%, 40–75 words — each with QuantitativeValue + citation attribution
• Speakable schema — TL;DR, the three-leg statement (Section 1), the passages-not-pages mechanic (Section 2), the checklist (Section 6), the take-home (Section 11)

Cross-platform distribution plan (the post should pass its own test):

• storimatic.ca/blog — primary publish with full schema + an embedded founder-on-camera clip whose attributed pull-quote (a single liftable sentence) sits at the top of the page
• YouTube long-form — 15-min “The 3-part test every video must now pass,” chaptered at: the test stated / passages not pages / definitive / structured / quotable / the checklist / the Library Remix; human-reviewed transcript; 334+ word description
• LinkedIn (Jared’s personal profile) — long-form article + 4 clips: the three-word test, the 44.2%-first-30% finding, “comprehensive loses / definitive wins,” the pre-shoot checklist
• YouTube Shorts cuts — one Short per leg (definitive / structured / quotable), each a single standalone soundbite
• Reddit — r/marketing, r/SEO, r/content_marketing: the counter-intuitive “comprehensive loses, definitive wins” finding as an answer-seed
• Email — Section 6 (the checklist) as a standalone, forwardable send for in-house marketers to run on their existing video library

Quarterly refresh:

• Q3 2026: re-verify the 44.2%-first-30% and 40–75-word passage figures against the latest extraction studies
• Q4 2026: add a verified Storimatic before/after — a sentence that got cited after passing the checklist (timestamped)
• Q1 2027: refresh the VideoObject inclusion figure and the Princeton magnitudes

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