inovruzova/polygraf-ner-stage1-cleaned

Polygraf / NER Stage 1: Corrected Starter Dataset

This dataset is the Stage 1 output of the Polygraf Applied NLP / NER technical task. It is derived from polygraf-ai/applied-nlp-ner-candidate-starter-100, whose annotations were deliberately made messy. The annotations here have been reviewed and corrected against the Labels definitions, the Baseline labeling rules, and the added policy, all of which are reproduced in full below.

The original starter dataset has not been modified or overwritten.

Summary

Item	Value
Records supplied	100
Records removed	5
Records cleaned of encoding corruption and re-annotated	6
Records in this dataset	95
Language	English
Task	Named entity recognition / token classification
Labels	PERSON, ORGANIZATION, LOCATION, TIMEDATE, PRODUCT, WORKOFART, JOB, AMOUNT

Record schema

Identical to the starter dataset.

• unique_index (int): the record identifier, preserved from the starter dataset
• source_text (string): the sentence or passage
• privacy_mask (list): labeled entities, each with
  • start (int): inclusive character start offset in source_text
  • end (int): exclusive character end offset in source_text
  • label (string): one of the eight labels
  • value (string): surface form, equal to source_text[start:end]

Every record in this dataset satisfies the following, checked by assertion at export time:

• every label is one of the eight declared labels
• source_text[start:end] == value for every span
• no span carries leading or trailing whitespace
• no two spans within a record overlap
• every unique_index is unique

Changes from the starter dataset

• Annotations corrected under the policy below.
• Five records removed as unresolvable rather than merely difficult. See the Removals section.
• Six records contained double round-tripped Mac Roman encoding corruption. Because repairing the text changes every character offset in the record, these were cleaned and annotated from scratch rather than patched.
• The starter annotations contained a ninth label, COMPANY, which is not part of the declared schema. All eight occurrences were resolved into the declared labels.

Removals

Five records were removed. The criterion was not difficulty but whether a consistent annotation exists at all, and whether including the record would degrade model behaviour on real text. Several difficult records were deliberately retained.

• 59 and 92: machine-generated word salad and spam. Entity-shaped tokens with no referent, where neither labeling nor not labeling yields a correct annotation.
• 61: OCR output with character-level corruption of the entity surfaces themselves, including one individual's surname appearing in two different spellings within the record.
• 80: scraped SEO keyword-stuffing including URL fragments, concatenated with LLM-generated filler prose. Absence of linguistic structure rather than policy underspecification.
• 91: dialogue whose speakers are anonymisation placeholders (#Person1#). Labeling them PERSON would teach the pattern #Word+Digit# equals PERSON, which would fire on hashtags and markup in real input.

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Labels

• PERSON: a named person, including given names and surnames.
  • Example: <PERSON>Barack Obama</PERSON> / <PERSON>Sarah Chen</PERSON>.
• ORGANIZATION: a named company or institution.
  • Example: <ORGANIZATION>Google</ORGANIZATION> / <ORGANIZATION>Mayo Clinic</ORGANIZATION>.
• LOCATION: a named place such as a city, country, region, street, landmark, or geographic area.
  • Example: <LOCATION>Berlin</LOCATION> / <LOCATION>New York City</LOCATION>.
• TIMEDATE: an expression that places something on a timeline, including dates, clock times, and durations used as time.
  • Example: <TIMEDATE>March 15, 2024</TIMEDATE> / <TIMEDATE>50 minutes</TIMEDATE>.
• PRODUCT: a named commercial product, device, or branded good.
  • Example: <PRODUCT>iPhone 15</PRODUCT> / <PRODUCT>MacBook Pro</PRODUCT>.
• WORKOFART: a named creative or published work such as a book, film, song, or titled publication.
  • Example: <WORKOFART>Oppenheimer</WORKOFART> / <WORKOFART>Spider-Man: Brand New Day</WORKOFART>.
• JOB: an occupational title or formal work role when it functions as such in the sentence.
  • Example: <JOB>software engineer</JOB> / <JOB>CEO</JOB>.
• AMOUNT: a measurable or countable quantity, not a time or date expression.
  • Example: <AMOUNT>50</AMOUNT> tickets / <AMOUNT>120,000</AMOUNT>.

Baseline labeling rules

These are the main rules. They are fixed and were not edited. Corrections and labeling decisions follow these main rules plus the added policy rules below.

• A labeled mention should cover the entity itself, not the surrounding grammar.
  • Example: Correct: She visited <LOCATION>Paris</LOCATION> yesterday. Incorrect: labeling visited Paris yesterday as one span.
• Articles, prepositions, conjunctions, and other function words should stay outside the span unless they are truly part of the proper name.
  • Example: Correct: He works at <ORGANIZATION>Google</ORGANIZATION>. Incorrect: <ORGANIZATION>at Google</ORGANIZATION>.
  • Example: <ORGANIZATION>The New York Times</ORGANIZATION> should keep The when it belongs to the established name.
• Bare type-words and category descriptors, meaning words that name a category rather than a specific entity, should not be labeled on their own. Examples include "person", "company", "organization", "team", "hospital", "city", "product", "book", and "quantity".
  • Example: The company hired 200 people. should leave company unlabeled.
  • Example: She joined <ORGANIZATION>Acme Corp</ORGANIZATION>. should still label the named organization.
  • Example: The person who called did not leave a name. should leave person unlabeled.
• A multi-word name should be one span when it forms one real named entity; separate entities should be labeled separately.
  • Example: <PERSON>Barack Obama</PERSON> should be one span.
  • Example: <PERSON>Barack</PERSON> and <PERSON>Michelle</PERSON> should be two spans.
• Coordinated names should be separate spans, unless the conjunction is part of one established name.
  • Example: <ORGANIZATION>Google</ORGANIZATION> and <ORGANIZATION>Microsoft</ORGANIZATION> should be two spans.
  • Example: <ORGANIZATION>Johnson & Johnson</ORGANIZATION> should stay one span because & belongs to the name.
• Ordinary punctuation and stray whitespace should stay outside spans, unless the punctuation belongs to the name or abbreviation.
  • Example: He moved to <LOCATION>Berlin</LOCATION>. should leave the final period outside the span.
  • Example: <ORGANIZATION>AT&T</ORGANIZATION> should keep & inside the span.
• Possessive markers should stay outside the span unless the full possessive form is the name.
  • Example: <PERSON>Maria</PERSON>'s laptop should leave 's outside the span.
• Labels should follow context, not surface form alone. The same words may be an organization in one sentence and a place in another.
  • Example: She works at <ORGANIZATION>Cambridge University</ORGANIZATION>.
  • Example: The conference was held in <LOCATION>Cambridge</LOCATION>.
• Quantities used as time should be labeled as TIMEDATE, not AMOUNT.
  • Example: She bought <AMOUNT>50</AMOUNT> tickets.
  • Example: The train arrives in <TIMEDATE>50 minutes</TIMEDATE>.

Added policy rules

These rules close gaps the baseline rules do not settle. They do not modify the baseline. Each is checked against the baseline and against the other added rules.

A. Quantity and time boundaries

R1. Only value-changing modifiers go inside the span. Modifiers that change the value denoted are included. Modifiers expressing the writer's stance, or positioning the value relative to a reference point, stay outside. <AMOUNT>about 50</AMOUNT> and <TIMEDATE>at least two years</TIMEDATE> include the modifier; only <TIMEDATE>two years</TIMEDATE> and <TIMEDATE>two years</TIMEDATE> away do not, because "only two years" still denotes exactly two years.

R2. Approximators and comparators are inside the span, whether they precede or follow the value: about, approximately, around, roughly, nearly, almost, at least, at most, more than, less than, over, under, up to, ~. <AMOUNT>about $784,700.78</AMOUNT>, <TIMEDATE>less than six hours a night</TIMEDATE>.

R3. Units stay outside; magnitude multipliers stay inside. Units of measurement and currency codes stay outside because they name what is measured. Multipliers (k, m, bn, million, billion) and percent stay inside because they change the value. <AMOUNT>25000</AMOUNT> square feet, <AMOUNT>884k</AMOUNT> Tunisian Dinar, <AMOUNT>3.5%</AMOUNT>.

R4. Currency symbol contiguity exception. Where an approximator precedes a number carrying a prefixed currency symbol, the span extends across the symbol to remain contiguous: <AMOUNT>about $150</AMOUNT>. The symbol is included only when it lies between two parts of the span, never on its own.

R5. Numeral-derived quantity words are AMOUNT even without an exact value: <AMOUNT>thousands</AMOUNT>, <AMOUNT>several hundred</AMOUNT>. A bare quantifier with no numeral to anchor it stays unlabeled: a few, several, many.

R6. Date ranges. Ranges with a connective (from X to Y, between X and Y) are two TIMEDATE spans with the connective outside. Ranges written with a dash and no connective are a single span: <TIMEDATE>2019-2021</TIMEDATE>.

B. What counts as TIMEDATE

R7. TIMEDATE requires a determinate point, date, or duration. Unbounded expressions are not labeled, however phrased: always, never, ever, now, yet, already, initially, at any hour of the day, round the clock, in the course of. The test is whether the expression restricts the timeline.

R8. Frequency expressions require a determinate interval. <TIMEDATE>every Saturday</TIMEDATE>, <TIMEDATE>twice a year</TIMEDATE> and <TIMEDATE>24-hour</TIMEDATE> qualify. Every time, whenever, every now and then do not.

R9. Deictic day-words (today, tonight, yesterday, tomorrow) are TIMEDATE when they locate a specific event relative to the text's moment, and unlabeled when they mean the present era generally. Test: can the word be swapped for a calendar date without changing the meaning?

R10. Named calendar observances, holidays and seasons are TIMEDATE. <TIMEDATE>Remembrance Day</TIMEDATE>, <TIMEDATE>Easter</TIMEDATE>, <TIMEDATE>Ramadan</TIMEDATE>. An observance recurs at a fixed calendar position, which is what the TIMEDATE definition asks for.

R11. Bare numbers are TIMEDATE only when context marks them as a date or time: a preposition of time, a surrounding date expression, or an explicit temporal frame. Otherwise AMOUNT. Sharing <AMOUNT>3339</AMOUNT>, but in <TIMEDATE>2021</TIMEDATE>.

C. PRODUCT boundaries

R12. Vintage, model and edition years preceding a product name are a separate TIMEDATE span, not part of the PRODUCT span, unless the year is part of the registered name. <TIMEDATE>2012</TIMEDATE> <PRODUCT>Dom Perignon Brut</PRODUCT>, but <PRODUCT>Windows 95</PRODUCT>.

R13. Spec-like tokens (grades, sizes, counts, strengths) stay inside the PRODUCT span when part of how the product is named and sold, and are AMOUNT only when they modify from outside. Test: does the product exist under that name without the token? <PRODUCT>5 Puttonyos Royal Tokaji</PRODUCT>, but <AMOUNT>two</AMOUNT> bottles of <PRODUCT>Dom Perignon</PRODUCT>.

R14. Brand names stay inside the PRODUCT span when they form the leading component of a commercial name. The same name is ORGANIZATION only when it refers to the company as an entity in its own right: acting, employing, being located, being acquired.

R15. Trailing category descriptors stay outside the PRODUCT span, with or without an intervening comma. <PRODUCT>Dom Perignon Brut</PRODUCT>, Champagne, <PRODUCT>M282</PRODUCT> warhead.

R16. Software, programming languages, frameworks and query languages are PRODUCT. <PRODUCT>Apache Hadoop</PRODUCT>, <PRODUCT>SQL</PRODUCT>, <PRODUCT>Django</PRODUCT>.

R17. Video games are PRODUCT; films, books, songs and albums are WORKOFART. A title in both forms takes the label matching the referent in context: <PRODUCT>Final Fantasy VII</PRODUCT> (game) versus <WORKOFART>Advent Children</WORKOFART> (film).

D. ORGANIZATION and LOCATION

R18. Named commercial establishments are ORGANIZATION. Restaurants, hotels, casinos, shops. LOCATION is reserved for geographic and administrative places. A locative phrase does not convert an establishment to LOCATION.

R19. Possessive prefixes on dish preparations. Label the establishment and leave the preparation unlabeled: <ORGANIZATION>Andre's</ORGANIZATION> Classic Lobster Thermidor. A possessive prefix attributes a generic preparation; it does not convert it into a PRODUCT.

R20. Metonymy. Place names used for the institution they house remain LOCATION when the name is primarily a place; names that are primarily institutional remain ORGANIZATION even when used locatively. Momentum on <LOCATION>Capitol Hill</LOCATION>, but <ORGANIZATION>The Pentagon</ORGANIZATION> lifted a ban.

R21. Sports teams, clubs and leagues are ORGANIZATION, including when the name begins with a city or region. The geographic component stays inside the span and is not separately labeled: <ORGANIZATION>Buffalo Sabres</ORGANIZATION>.

E. PERSON and JOB

R22. Honorifics stay outside the PERSON span: Dr., Mr., Mrs., Ms., Prof., Sir, Rev. Dr. <PERSON>Amanda Martinez</PERSON>.

R23. Occupational words used as stand-in names are PERSON, not JOB. <PERSON>Guitarist</PERSON>: great player (a nickname for one performer) versus the <JOB>guitarist</JOB> was late (the role).

R24. Generic collective nouns for people are not JOB: officials, authorities, staff, employees, management, workers, residents, sources. A modifier can make one specific enough to qualify: <JOB>health officials</JOB>.

R25. Names of academic or professional fields are not JOB (psychology, finance, engineering). JOB requires a word denoting a person's role.

R26. Usernames and handles that denote a specific individual are PERSON: <PERSON>otavio.telin</PERSON>, <PERSON>@xbllygbsn</PERSON>. Opaque identifiers with no name component are unlabeled.

R27. Fictional characters and mascots are PERSON. <PERSON>Ronald McDonald</PERSON>, <PERSON>Cait Sith</PERSON>.

F. Cross-cutting

R28. Entities with no home label stay unlabeled: nationalities and ethnic groups, languages, and named events. Note the deliberate contrast with R10: a calendar observance marks a position on a timeline and fits TIMEDATE; a named event does not.

R29. Idiomatic and figurative uses are not labeled. A span is labeled only when it refers to an actual instance of the entity type. such May-December couples is unlabeled, since no month is referred to.

R30. Nested entities take the outer span only. The schema is flat and the training format admits no overlaps.

R31. Citation and document apparatus is not labeled: footnote markers, page and docket references, section symbols, internal cross-references.

R32. Named awards and prizes are WORKOFART.

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Intended use and licensing

The source_text passages originate from the Polygraf starter dataset, which redistributes short excerpts of publicly available English text under an evaluation-use-only licence for the purpose of the Polygraf Applied NLP / NER technical task. The starter dataset's terms permit deriving a corrected dataset from it and publishing that derivative for that purpose. This dataset is that derivative and is published for that purpose only.

Rights in the underlying source passages remain with their original holders. Any reuse outside the technical task should be checked against the terms of the original sources and of the starter dataset.

The annotations in this dataset are corrections and additions produced during the technical task.

Reference

Klie, J.-C., Webber, B., and Gurevych, I. (2023). Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future. The triage design in this stage follows the Retag and variation n-gram methods described there, with error detection framed as a ranking task.