# Real entity, fabricated work product Part of [The AI Tells Index](https://feedsquad.com/ai-tells). A tell signals low effort. It does not identify an author. Skilled writers produce every shape listed here, some of them daily, and automated detectors misread those writers for it at rates measured above 60 percent on non-native English prose. Nothing in this index proves that a machine wrote anything. Read an entry as one piece of evidence to weigh against the false-positive notes printed beside it. ## Facts - Id: real-entity-fabricated-work - Category: Semantic (semantic) - Subcategory: fabrication - Also known as: fake book by a real author, invented credential at a real institution, fabricated quote from a real person, I never said that - Status: Active. Currently signals low-effort writing. - Severity: high - Evidence grade: corroborated (named independently by multiple credible secondary sources) - Languages: en - Added: 2026-08-15 - Updated: 2026-08-15 - Page: https://feedsquad.com/ai-tells/real-entity-fabricated-work ## Description A person, institution or company that a reader can look up, attached to a book, quotation, study or credential that does not exist. The entity checks out. The work product hung on it does not. Half the sentence survives verification, which is what makes the other half so hard to see. ## Why it reads as machine-written A name and a plausible title for that name come out of the same distribution, so they arrive together with nothing binding them. Nothing in the text records whether the book was ever opened. The best documented case is a newspaper auditing itself: the Chicago Sun-Times review of the syndicated section it printed in May 2025 found recommended titles credited to living authors who had never written them, an expert placed at a university that had not employed her, and quoted remarks from a named blogger who told reporters he had said nothing of the kind. The review noted that every story carrying named sources carried errors, which inverts the normal editorial heuristic. ## Detection Type: judge (a rubric for a lightweight model judge) Rubric for a lightweight model judge: ```text Task: find places where a checkable entity carries an uncheckable work product. Step 1. List every named person, institution, company, journal, award or court in the text. Step 2. For each, list what the text attributes to it: a title, a quotation, a credential, an affiliation, a study, a finding, a ruling. Step 3. Mark the entity VERIFIED or UNVERIFIED by looking it up. Step 4. Mark each attribution VERIFIED, UNRESOLVED or ABSENT. UNRESOLVED means the entity's own site, catalogue or register returns nothing under that title, that person or that year. Escape hatches, which return PASS for that item: the text marks the attribution as paraphrase from memory; the work is stated as forthcoming, unpublished, internal or under embargo; the source is a private communication the text identifies as one; the entity is presented as fictional; the reviewer cannot reach the catalogue at all, which is unknown rather than absent. Decision. FLAG when at least one attribution is UNRESOLVED and the entity carrying it is VERIFIED. That exact pairing is the pattern. Return WEAK when the entity is also UNVERIFIED, because that is a different failure and a different entry. Return PASS otherwise. Output: each entity, its attribution, both marks, and one of FLAG / WEAK / PASS. State nothing about who wrote the text. ``` ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > Dr. Helena Marsh of the Northfield Institute for Consumer Trust found in her 2023 study Signals That Sell that shoppers abandon a checkout within four seconds of seeing an unexpected fee. After, repaired: > We logged 1,412 checkouts in June. Thirty-eight percent of the abandonments happened on the screen where shipping was added. That number is ours, from our own funnel, and anyone who wants to argue with it can ask how we counted. Note: The researcher, the institute and the study in the first version are invented for this specimen. The repair drops the borrowed standing and uses a figure the writer can defend. Figures in this repair are invented for the specimen. ### Specimen 2 Before, exhibiting the tell: > Our method was reviewed by a former lead assessor for the National Standards Board of Applied Analytics, who confirmed it meets the current bar. After, repaired: > Our advisor reviewed the method on 4 July and has agreed to be named in the appendix, with her review notes published beside it. There is no board and no bar. There is one person, named, whose objections you can read. Note: The board in the first version does not exist. That is the whole of the problem, and no amount of rewording fixes it. ## False positives Who legitimately writes this way. Ordinary citation error produces the same surface. A transposed year, a title remembered a word wrong, an author confused with a co-author, a book reissued under a different title in another market: all of these leave a real name attached to a reference that will not resolve. Catalogues are also incomplete, and a great deal of pre-1990 trade publishing was never indexed at all. Verification has to come before any accusation, and the correct first move is to ask the writer for the source rather than to reach a conclusion about how the text was produced. A writer who supplies a scan or a shelf mark has answered the question. A writer who supplies another unresolvable reference has not. ## Model attribution No published work assigns this to a vendor. The documented cases run across several years and several assistants, and every one of them reached print through a pipeline where nobody checked a citation. ## Sources 1. Chicago Sun-Times, special section with fake book list plagued with additional errors https://chicago.suntimes.com/news/2025/05/29/special-section-king-fake-book-list-errors-sun-times-review (tier: press; accessed 2026-08-14) 2. Snopes, Chicago Sun-Times AI reading list fact check https://www.snopes.com/fact-check/chicago-sun-times-ai-reading-list/ (tier: press; accessed 2026-08-14) 3. NPR, fake summer reading list https://npr.org/2025/05/20/nx-s1-5405022/fake-summer-reading-list-ai (tier: press; accessed 2026-08-14) ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-15, Active: A newspaper published its own audit of the failure in May 2025, naming fake titles credited to real authors, a fabricated expert at a real university and invented quotes from a real person. Snopes and NPR corroborate the same section. Self-incriminating evidence, which is the strongest kind available here. ## License CC BY 4.0. https://creativecommons.org/licenses/by/4.0/ Attribution: The AI Tells Index, feedsquad.com/ai-tells Reuse the data, including commercially. Keep the attribution line. --- Dataset version 1.0.0. Schema version 1. Part of [FeedSquad](https://feedsquad.com). Built by [Herman Foundry](https://hermanfoundry.com) from Levi, Finnish Lapland.