Hasanblanka/polygraf-ner-stage1-cleaned

Polygraf NER Stage 1 Cleaned Dataset

This repository contains the corrected result of Stage 1 - Rulecraft and Cleanup for the Polygraf Applied NLP / NER technical project.

Source

• Source dataset: polygraf-ai/applied-nlp-ner-candidate-starter-100
• Pinned source revision: 11cabd333e6d16531b8dd96d2198bc291d383cd3
• Raw canonical JSONL SHA256: 25adafdcbb182fcb524b458abeca3e25ef073c519804783f6b06f9d933609b5a
• Policy version: 1.0

The raw starter dataset was not overwritten. Corrections were stored in a manifest and applied to a copy of the raw records.

Dataset Files

• data/stage1_cleaned.parquet: default Hugging Face dataset file.
• data/stage1_cleaned.jsonl: equivalent canonical JSONL representation.
• annotation_policy.md: standalone copy of the full annotation policy.
• LICENSE: source dataset's evaluation-use-only license.

Each record has:

{
  "unique_index": 1,
  "source_text": "Example text",
  "privacy_mask": [
    {
      "start": 0,
      "end": 7,
      "label": "PERSON",
      "value": "Example"
    }
  ]
}

Offsets use Python-style half-open intervals: source_text[start:end].

Cleanup Summary

Metric	Raw	Clean
Records	100	90
Spans	835	609
Changed records	-	76
Unchanged records	-	14
Removed records	0	10
Automatic errors	8	0
Manually accepted warnings	-	17

Label Counts

Label	Raw	Clean
PERSON	153	91
ORGANIZATION	80	82
LOCATION	101	60
TIMEDATE	164	122
PRODUCT	87	81
WORKOFART	59	50
JOB	100	58
AMOUNT	83	65
Invalid COMPANY	8	0

All 100 raw records were manually reviewed. All 90 retained records passed a second manual QA review. The remaining 17 automatic warnings were reviewed and accepted as valid official names, abbreviations, possessive official names, or complete duration/frequency spans.

Removed Records

Removals were applied only to this processed dataset:

• ID 59: a long unrelated random-word fragment after a useful opening.
• IDs 60, 68, 69, 71, and 75: severe recurring encoding corruption.
• ID 61: severe OCR errors that prevent reliable span selection.
• ID 80: keyword spam and nonsensical word lists dominate the text.
• ID 81: severe multi-layer encoding corruption.
• ID 92: generated nonsense and mixed-language fragments dominate the record.

The records were removed under policy rule A9 because they were likely to harm model behavior, not because they were merely difficult to annotate.

Loading

from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/polygraf-ner-stage1-cleaned")
print(dataset["train"][0])

The JSONL file can also be loaded directly:

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files="data/stage1_cleaned.jsonl",
)

Labels

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

Fixed Baseline Labeling Rules

These rules are fixed. Corrections follow these rules together with the extra policy rules below.

1. A labeled mention should cover the entity itself, not the surrounding grammar.
   • Correct: She visited <LOCATION>Paris</LOCATION> yesterday.
   • Incorrect: labeling visited Paris yesterday as one span.
2. Articles, prepositions, conjunctions, and other function words should stay outside the span unless they are truly part of the proper name.
   • Correct: He works at <ORGANIZATION>Google</ORGANIZATION>.
   • <ORGANIZATION>The New York Times</ORGANIZATION> keeps The because it belongs to the established name.
3. Bare type words and category descriptors should not be labeled on their own. Examples include person, company, organization, team, hospital, city, product, book, and quantity.
   • The company hired 200 people. leaves company unlabeled.
   • She joined <ORGANIZATION>Acme Corp</ORGANIZATION>. labels the named organization.
4. A multi-word name should be one span when it forms one real named entity; separate entities should be labeled separately.
   • <PERSON>Barack Obama</PERSON> is one span.
   • <PERSON>Barack</PERSON> and <PERSON>Michelle</PERSON> are two spans.
5. Coordinated names should be separate spans unless the conjunction is part of one established name.
   • <ORGANIZATION>Google</ORGANIZATION> and <ORGANIZATION>Microsoft</ORGANIZATION> are separate.
   • <ORGANIZATION>Johnson & Johnson</ORGANIZATION> stays together.
6. Ordinary punctuation and stray whitespace should stay outside spans unless the punctuation belongs to the name or abbreviation.
   • He moved to <LOCATION>Berlin</LOCATION>.
   • <ORGANIZATION>AT&T</ORGANIZATION> keeps &.
7. Possessive markers should stay outside the span unless the full possessive form is the name.
   • <PERSON>Maria</PERSON>'s laptop.
8. Labels should follow context, not surface form alone.
   • She works at <ORGANIZATION>Cambridge University</ORGANIZATION>.
   • The conference was held in <LOCATION>Cambridge</LOCATION>.
9. Quantities used as time should be labeled TIMEDATE, not AMOUNT.
   • She bought <AMOUNT>50</AMOUNT> tickets.
   • The train arrives in <TIMEDATE>50 minutes</TIMEDATE>.

Additional Policy Rules

A1. Temporal specificity, ages, and vague temporal adverbs

Calendar dates, clock times, seasons, durations, frequencies, ages, and relative/deictic expressions with a concrete temporal reference are labeled TIMEDATE. General adverbs that do not identify a time point or interval, such as ever, never, always, again, yet, just, initially, and already, are not labeled. Interrogative placeholders such as when and how long ask for a temporal value but do not provide one, so they are not labeled.

• Record 45: 19-year-old is an age and is labeled TIMEDATE.
• Record 72: Now and three years later place events on the timeline.
• Record 10: standalone ever, never, and before are not labeled.
• Record 58: How long asks for a duration but does not express one.

Reason: this prevents general discourse adverbs and question placeholders from inflating the time class while retaining real temporal references.

A2. Core quantity spans and unit boundaries

An AMOUNT span includes the numeric/count component, ranges, and scale words: 3.5, 3-4, 1.2 million, half, both, and thousands. Currency symbols and codes, measurement units, counted nouns, and approximation modifiers such as about, over, and at least stay outside.

Digits alone are not sufficient for AMOUNT. A numeric string used as a code, identifier, model name, or shared value is not labeled unless it expresses a measurable or countable quantity.

For TIMEDATE durations and ages, the time unit determines the class and stays inside the span (15 minutes, 19-year-old), while external modifiers such as about and up to stay outside.

• Record 33: about $ <AMOUNT>784,700.78</AMOUNT>.
• Record 85: <AMOUNT>6</AMOUNT>ft and <TIMEDATE>two months ago</TIMEDATE>.
• Record 9: less than <TIMEDATE>six hours</TIMEDATE> a night.
• Record 29: 3339 and 483 are numeric identifiers, not quantities.

Reason: this applies the baseline minimal-span principle consistently and prevents identifiers from being learned as quantities.

A3. Person names, nicknames, and usernames

Adjacent given and family names referring to one person form one PERSON span. Titles and possessive markers stay outside. A nickname inside a full name may remain inside the same span, including its quotation marks. A username or explicitly introduced nickname is labeled only when the context presents it as a real person reference. Structural dialogue placeholders are not labeled.

• Record 1: <PERSON>Manny Pacquiao</PERSON>, not two separate spans.
• Record 73: <PERSON>Norville "Shaggy" Rogers</PERSON>.
• Record 91: Person1 and Person2 are structural placeholders.

Reason: this extends the baseline multi-word name rule consistently to nicknames and usernames.

A4. Product, organization, and published work

A named application, game, platform, medicine, font, device, or branded good is PRODUCT when the context refers to the artifact or service. The company that operates or manufactures it is ORGANIZATION. A film, song, book, article, report, or other publication title is WORKOFART.

• Record 13: Discord is PRODUCT in the context on Discord.
• Record 26: Garamond is a font and is PRODUCT.
• Record 99: Project Performance Audit Report is WORKOFART.

Reason: this resolves recurring semantic confusion while following the baseline context rule.

A5. Occupations versus departments and activities

JOB is used only for an occupational title or formal work role in the sentence. A department, team, sector, field, activity, or generic organizational function is not JOB. A specifically named department may be ORGANIZATION.

• Record 66: Business Intelligence Director is JOB; market analysis division and analytics team are unlabeled.
• Record 77: Geriatric Medicine Department is ORGANIZATION, not JOB.
• Record 80: psychology and military are not occupations.

Reason: this prevents topics and organizational units from being learned as occupations.

A6. Locations, establishments, and companies

Named companies, sports teams, restaurants, casinos, and establishments remain ORGANIZATION even when someone visits them. Geographic areas, streets, addresses, landmarks, and physical facilities are LOCATION when the context emphasizes the place. The same surface form may therefore receive different labels in different contexts.

• Record 65: Joyride Taco House is a restaurant and is ORGANIZATION.
• Record 69: McDonald's is ORGANIZATION when referring to the company, but may be LOCATION when the text explicitly refers to being inside a physical restaurant.
• Record 56: West Wing Studio is LOCATION when described as a physical facility.

Reason: this makes the baseline context rule explicit for establishment/place confusion.

A7. No nested labels inside work titles

The dataset uses flat, non-overlapping spans. If another entity name appears inside a complete work title, the whole title is labeled WORKOFART and no nested span is created.

• Record 87: <WORKOFART>Diversity Rates in Mercer County, West Virginia Evaluation Essay</WORKOFART> is one span.

Reason: this keeps annotations compatible with flat BIO token classification and removes conflicting overlaps.

A8. Proposed names

An expression explicitly proposed as a name is labeled according to the intended entity type even if the object has not yet been released.

• Record 2: proposed publication titles are WORKOFART.
• Record 96: proposed pet-collar product names are PRODUCT.

Reason: the text explicitly presents these expressions as names; release status does not change the naming context.

A9. Record-removal quality threshold

A record is removed only when severe OCR/encoding corruption, an unrelated nonsensical word list, or truncation makes reliable annotation impossible. Ordinary spelling and grammar errors are retained as real language variation.

• Record 61: severe OCR corruption prevents reliable span selection.
• Record 80: keyword spam and nonsensical lists dominate the text.
• Record 92: generated nonsense and mixed-language fragments dominate the record.

Reason: removal is reserved for objective quality failures likely to harm the model, not used to avoid difficult annotation decisions.

Quality and Limitations

• This is a small 90-record Stage 1 seed dataset, not a final balanced training corpus.
• No train/test split is provided at this stage.
• Ordinary spelling and grammar variation was intentionally retained.
• The remaining 17 automatic warnings are documented manual exceptions, not unresolved structural errors.
• The source excerpts remain subject to the evaluation-use-only license in LICENSE.

License

This derived dataset follows the source dataset's evaluation-use-only license. Permission covers completion of the Polygraf Applied NLP / NER technical project, including publication of a corrected or expanded derived dataset. See LICENSE for the complete terms.