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Falsehoods programmers believe about address format

Falsehoods​

Programmers consistently assume addresses are written in ASCII, follow a fixed ordering, don't contain special characters, and stay the same over time. Real addresses violate all of these — and the violations compound: an address that uses Cyrillic, in non-Western ordering, with an apostrophe in the streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. name, at a location that changed jurisdictions last year.

"Addresses are written in ASCII."​

The Greek tax office: Χανδρή 1 & Θεσσαλονίκης, Τ.Κ. 18346, Αθήνα. A Russian parcelparcelA property polygon or record carrying a situs (site) address and often a separate owner mailing address. County GIS parcel aggregations are a training source for address-point variety and situs-vs-owner divergence. with a Cyrillic address labelcomponent tagOne of the 33 labels in Mailwoman's address schema — street, locality, region, postcode, house_number, unit, po_box, country, venue, intersection, and others. Each parsed span carries exactly one component tag.. A Japanese address: 東京都千代田区丸の内1-1-1. A Chinese address: 北京市东城区东长安街1号. An Arabic address: شارع الملك فهد, الرياض.

Rule-based parsers that operate on ASCII character ranges ([a-zA-Z] for streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. names, \d for numbers) are blind to non-Latin scripts. The tokenizertokenizerThe component that converts a raw address string into a sequence of numeric token IDs the model can process. Mailwoman's tokenizer is a SentencePiece unigram model trained specifically on postal addresses. must handle multi-byte characters, the classifier must recognize script-specific patterns, and the resolverresolverThe component that converts parsed address components (locality, region, postcode) into coordinates by looking them up in the gazetteer. The resolver ranks candidates by name match, population, and proximity, and returns the best-matching place with its centroid or polygon. must match names in the script they were written in.

Mailwoman's SentencePieceSentencePieceA language-independent subword tokenizer that splits text into pieces using a unigram language model. Mailwoman uses a SentencePiece tokenizer with a 48,000-token vocabulary and byte-fallback, trained on address data rather than general text. tokenizertokenizerThe component that converts a raw address string into a sequence of numeric token IDs the model can process. Mailwoman's tokenizer is a SentencePiece unigram model trained specifically on postal addresses. uses byte_fallback=true — when a character is not in the trained vocabularyvocabularyThe fixed set of tokens a tokenizer can produce. Mailwoman's SentencePiece vocabulary is tens of thousands of subword pieces, with byte fallback for anything outside it., the tokenizertokenizerThe component that converts a raw address string into a sequence of numeric token IDs the model can process. Mailwoman's tokenizer is a SentencePiece unigram model trained specifically on postal addresses. falls back to byte-level encoding. This means the tokenizertokenizerThe component that converts a raw address string into a sequence of numeric token IDs the model can process. Mailwoman's tokenizer is a SentencePiece unigram model trained specifically on postal addresses. can handle any Unicode character, even scripts it was not explicitly trained on. The A1 tokenizertokenizerThe component that converts a raw address string into a sequence of numeric token IDs the model can process. Mailwoman's tokenizer is a SentencePiece unigram model trained specifically on postal addresses. (v0.5.0) halved the byte-fallback rate from 36.7% to 18.2% by trainingtrainingThe process of adjusting a model's parameters so its predictions match labeled examples, by repeatedly measuring error and nudging the weights to reduce it. Distinct from inference, when the trained model is run on new input. on a corpuscorpusThe BIO-labeled training data used to train Mailwoman's neural classifier. Assembled from real sources (OpenAddresses, National Address Database) and synthetic shards (boundary stress, order variants, negative space). Managed by @mailwoman/corpus. that includes CJK, Cyrillic, Hangul, Han, and Armenian scripts.

"Addresses follow a fixed ordering."​

The United States uses most-specific-to-least-specific: [number] [street] [city] [state] [zip]. Japan uses least-specific-to-most-specific: [zip] [prefecture] [city] [ward] [district] [block] [lot]. Hungary uses three different orders depending on how many lines are available:

  • One line: {zip} {town}, {street} {buildingNr}
  • Multiple lines: {street} {buildingNr}. {zip} {town}, {country}
  • Envelope: {street} {buildingNr}. {town} {zip} {country}

The same address, three different written orders. A parser that assumes a single ordering will successfully parseaddress parsingThe process of decomposing a free-text postal address string into structured components — house number, street name, locality, region, postcode, and country — so a geocoder can resolve them to coordinates. one form and fail on the other two.

European addresses often place the building number after the streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. name: République 12 (France), Hauptstraße 5 (Germany), Kornetintie 6 A II krs (Finland). The Anglophone pattern of number-before-streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. is the exception.

"Addresses don't contain special characters."​

St. Judes & St. Pauls C of E (Va) Primary School, 10 Kingsbury Road, London, N1 4AZ contains an ampersand, parentheses, a comma, and a period. 1 Acre View, Bo'ness, EH51 9RQ contains an apostrophe. 1 Highview Terrace, Westward Ho!, Bideford, EX39 1AQ contains an exclamation mark (omitted in some databases). Flat 1.4, Ziggurat Building, 60-66 Saffron Hill, London, EC1N 8QX contains a decimal point in the flat number.

Kirkland, Lane, Mathias & Perry, North Muskham Prebend Church Street, Southwell, NG25 0HQ contains two commas and an ampersand — in the organization name, not as address separators.

The Hague's official Dutch name is 's-Gravenhage — starting with an apostrophe-s and containing a hyphen. A parser that strips punctuation from the start of tokenstokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words. will reduce 's-Gravenhage to s-Gravenhage — a different name.

"A single address uses a single character set."​

International mail often includes the destination countrycountryThe top-level address component (an ISO country). Closed-vocabulary, so it is best handled by a deterministic matcher feeding a proposal rather than a retrained model head. in both the source and destination character sets, so postal workers at both ends can read it. A parcelparcelA property polygon or record carrying a situs (site) address and often a separate owner mailing address. County GIS parcel aggregations are a training source for address-point variety and situs-vs-owner divergence. from Japan to France might have France written in both Latin and Japanese scripts. A parser that expects a single character set per address will see two countrycountryThe top-level address component (an ISO country). Closed-vocabulary, so it is best handled by a deterministic matcher feeding a proposal rather than a retrained model head. names at different positions in different scripts.

"Written addresses don't change."​

Douglas Perreault's condo in Florida changed address three times: 14100 N 46th St., Alpha 39, Lutz, FL 33549 → Tampa, FL 33612 (new post office) → FL 33613 (ZIP Code change) → 14410 Hanging Moss Circle, #101 (block renaming). Same physical location, four different written addresses over a few years.

Cities, streetsstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels., and entire countries get renamed. The county of Gwent, UK no longer exists. Lenin Street became something else across Eastern Europe. Territory disputes change which administrative regionregionThe first-level administrative subdivision of a country — a US state, a French region, a province. The component between country and locality. an address is officially in — sometimes without changing the building's physical location.

"An address fits in 5 lines."​

Department For Environment Food & Rural Affairs (D E F R A), State Veterinary Service, Animal Health Office, Hadrian House, Wavell Drive, Rosehill Industrial Estate, Carlisle, CA1 2TB, United Kingdom needs 8 lines. Addresses that include organization names, department names, and industrial-park locations routinely exceed the 5-line assumption built into address forms.

"Street names are short."​

The longest streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. name in Germany: Bischöflich-Geistlicher-Rat-Josef-Zinnbauer-Straße in Dingolfing, Bavaria. An 89-character streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. name in Bihać, Bosnia: Aleja Alije Izetbegovića Prvig Predsjednika Predsjedništva Republika Bosna i Hercegovina. A form field with a 50-character limit on streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. names rejects real addresses.

"Real place names won't contain rude words."​

Middlesex, Scunthorpe, Penistone, and many others are real English place namestoponymA proper name for a geographic place.. Address validators that filter for profanity reject real places. The Scunthorpe problem (named for the town whose name contains a substring interpreted as profanity) is a classic example of overzealous content filtering.

How traditional geocoders handled these​

libpostallibpostalAn open-source C address parser used by Pelias. Mailwoman's rule-based v0 and neural classifier supersede it. handles multi-script addresses through its CRFCRF (Conditional Random Field). A statistical modeling method that predicts structured outputs by modeling dependencies between adjacent labels. Mailwoman uses a linear-chain CRF as the Viterbi decoder at inference time to enforce BIO label consistency — a B-street must be followed by I-street or O, never I-locality.'s character-level featuresfeatureAn input signal a model conditions on. Beyond the raw tokens, Mailwoman feeds soft features — gazetteer-membership channels and the postcode anchor — that inform predictions without overriding them.. The modelneural classifierThe machine learning model at the core of Mailwoman's parser — a transformer encoder (~30M parameters) trained from scratch to do BIO token classification over addresses. It learns the 'grammar' of address formats; the gazetteer supplies the 'atlas.' operates on character n-gramsn-gramA contiguous run of n tokens (a bigram is 2, a trigram is 3). Counting n-grams is a simple, pre-neural way to model which token sequences are common., not words, so it can handle any script without script-specific tokenization. libpostallibpostalAn open-source C address parser used by Pelias. Mailwoman's rule-based v0 and neural classifier supersede it.'s normalize step handles Unicode normalization (NFD → NFC) and transliterationtransliterationConverting a name from one writing system to another while preserving pronunciation (Cyrillic → Latin, for instance). Needed for multilingual address handling and corpus synthesis., but it does not handle mixed-script addresses (different scripts in different components).

PeliasPeliasAn open-source geocoder, Mailwoman's spiritual predecessor. tokenizes on whitespace and punctuation. Non-ASCII characters are preserved in the tokentokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words. stream. PeliasPeliasAn open-source geocoder, Mailwoman's spiritual predecessor.'s rule classifiers operate on the tokentokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words. text directly — if a tokentokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words. matches a dictionary entry, it classifies; if not, it moves on. This means PeliasPeliasAn open-source geocoder, Mailwoman's spiritual predecessor. can handle non-ASCII names that appear in its dictionaries (WOFWOF (Who's On First). An open-source gazetteer of places maintained by Mapzen/whosonfirst. Mailwoman builds a custom SQLite database from WOF GeoJSON repos, extended with postcode data, importance scores, and coincident-role relations. gazetteergazetteerA geographical index that maps place names and postcodes to real-world coordinates. Mailwoman uses a custom-built Who's On First (WOF) SQLite database as its gazetteer — the 'atlas' half of the grammar/atlas architecture. entries, OpenStreetMapOpenStreetMap (OSM). A community-curated global map database (ODbL-licensed) with addr:* tagged features and place hierarchies. A secondary corpus source and a source of street names. tags) but not names in scripts that are not well-represented in open data.

Google's API handles multi-script, multi-lingual addresses comprehensively. Google's modelneural classifierThe machine learning model at the core of Mailwoman's parser — a transformer encoder (~30M parameters) trained from scratch to do BIO token classification over addresses. It learns the 'grammar' of address formats; the gazetteer supplies the 'atlas.' is trained on global address data and can parseaddress parsingThe process of decomposing a free-text postal address string into structured components — house number, street name, locality, region, postcode, and country — so a geocoder can resolve them to coordinates. addresses in dozens of scripts. The trade-off is that the modelneural classifierThe machine learning model at the core of Mailwoman's parser — a transformer encoder (~30M parameters) trained from scratch to do BIO token classification over addresses. It learns the 'grammar' of address formats; the gazetteer supplies the 'atlas.' is proprietary — you cannot inspect how it handles a specific script or format.

What the neural approach changes​

The normalizer (StagestageOne of the dataflow stages in the runtime pipeline (normalize, locale gate, kind classify, phrase group, token classify, sequence correct, reconcile, resolve). Distinct from tier (model vocabulary) and phase (plan milestone). 1) handles Unicode normalization (NFD → NFC), case folding, and whitespace collapsing. The normalizer operates on bytes, not semantics — it cleans the input for downstream stagesstageOne of the dataflow stages in the runtime pipeline (normalize, locale gate, kind classify, phrase group, token classify, sequence correct, reconcile, resolve). Distinct from tier (model vocabulary) and phase (plan milestone). without making classification decisions.

The tokenizer uses SentencePieceSentencePieceA language-independent subword tokenizer that splits text into pieces using a unigram language model. Mailwoman uses a SentencePiece tokenizer with a 48,000-token vocabulary and byte-fallback, trained on address data rather than general text. with byte-fallback. Characters in the trained vocabularyvocabularyThe fixed set of tokens a tokenizer can produce. Mailwoman's SentencePiece vocabulary is tens of thousands of subword pieces, with byte fallback for anything outside it. (16K for A0, 48K for A1) produce subword tokenstokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words. with known embeddingsembeddingA vector of numbers representing a token (or other item) so that similar items sit near each other in vector space. The first thing the model does is turn each token into an embedding.. Characters outside the vocabularyvocabularyThe fixed set of tokens a tokenizer can produce. Mailwoman's SentencePiece vocabulary is tens of thousands of subword pieces, with byte fallback for anything outside it. fall back to byte-level encoding — the modelneural classifierThe machine learning model at the core of Mailwoman's parser — a transformer encoder (~30M parameters) trained from scratch to do BIO token classification over addresses. It learns the 'grammar' of address formats; the gazetteer supplies the 'atlas.' sees raw bytes and learns byte-level patterns. This means the tokenizertokenizerThe component that converts a raw address string into a sequence of numeric token IDs the model can process. Mailwoman's tokenizer is a SentencePiece unigram model trained specifically on postal addresses. can handle any Unicode character, even scripts that were not in the trainingtrainingThe process of adjusting a model's parameters so its predictions match labeled examples, by repeatedly measuring error and nudging the weights to reduce it. Distinct from inference, when the trained model is run on new input. data, without crashing or producing garbage.

The locale gatelocale gateStage 2 of the runtime pipeline: rule-based locale detection from the query shape's script and known-format signals. Returns a LocaleHint with the top candidate and alternatives, surfacing disagreement with an explicit --locale flag. (StagestageOne of the dataflow stages in the runtime pipeline (normalize, locale gate, kind classify, phrase group, token classify, sequence correct, reconcile, resolve). Distinct from tier (model vocabulary) and phase (plan milestone). 2) detects the script family before classification. CJK characters → ja-JP, Cyrillic → ru, Arabic → ar. The localelocaleThe combination of language and country an address comes from. en-US and fr-FR are the locales Mailwoman ships weights for. detection biases the classifier's expectations: a Japanese-formatted address triggers Japanese ordering expectations, a Cyrillic address triggers Russian postcodepostcodeThe country-specific postal code (US ZIP, French code postal, etc.). Mailwoman handles postcode parsing entirely by rule classifier — a regex problem, not an ML one. patterns. The classifier does not need to handle every script's ordering simultaneously.

The phrase grouperphrase grouperStage 2.7 of the runtime pipeline: proposes coherent input units (street phrase, locality phrase, postcode, etc.) with structural kind hypotheses. Decouples boundary discovery from type classification so the classifier answers 'what type?' not 'where?' (StagestageOne of the dataflow stages in the runtime pipeline (normalize, locale gate, kind classify, phrase group, token classify, sequence correct, reconcile, resolve). Distinct from tier (model vocabulary) and phase (plan milestone). 2.7) uses structural cues (punctuation, whitespace, capitalization) that are script-agnostic. An apostrophe in Bo'ness does not confuse the grouper — it proposes Bo'ness as a single spanspanA contiguous range of characters or tokens in the input string, tagged with an address component type (street, locality, postcode, etc.). Parsed addresses are represented as collections of spans, possibly nested in a tree. because the apostrophe is word-internal punctuation, not a tokentokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words. boundary. An exclamation mark in Westward Ho! is similarly treated as part of the tokentokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words., not a separator.

The classifier (StagestageOne of the dataflow stages in the runtime pipeline (normalize, locale gate, kind classify, phrase group, token classify, sequence correct, reconcile, resolve). Distinct from tier (model vocabulary) and phase (plan milestone). 3) learns from the corpuscorpusThe BIO-labeled training data used to train Mailwoman's neural classifier. Assembled from real sources (OpenAddresses, National Address Database) and synthetic shards (boundary stress, order variants, negative space). Managed by @mailwoman/corpus.: tokenstokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words. appearing in address contexts with certain structural featuresfeatureAn input signal a model conditions on. Beyond the raw tokens, Mailwoman feeds soft features — gazetteer-membership channels and the postcode anchor — that inform predictions without overriding them. (position, surrounding tokenstokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words., phrase grouperphrase grouperStage 2.7 of the runtime pipeline: proposes coherent input units (street phrase, locality phrase, postcode, etc.) with structural kind hypotheses. Decouples boundary discovery from type classification so the classifier answers 'what type?' not 'where?' kind hypothesis) are classified based on learned distributions, not dictionary matches. A Cyrillic streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. name is classified as street because the trainingtrainingThe process of adjusting a model's parameters so its predictions match labeled examples, by repeatedly measuring error and nudging the weights to reduce it. Distinct from inference, when the trained model is run on new input. data contains Cyrillic streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. names in streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. positions — not because a Cyrillic dictionary exists.

What Mailwoman still can't do​

  • Mixed-script addresses. An address like Moscow, ул. Тверская 12 — English citylocalityThe city / town / settlement component of an address: a populated place sitting between region and neighbourhood in the hierarchy., Cyrillic streetstreetThe named linear feature along which house numbers are ordered. Decomposes into a name plus street affixes; one of the Tier 2 fine labels. — requires the modelneural classifierThe machine learning model at the core of Mailwoman's parser — a transformer encoder (~30M parameters) trained from scratch to do BIO token classification over addresses. It learns the 'grammar' of address formats; the gazetteer supplies the 'atlas.' to handle two scripts in one input. The tokenizertokenizerThe component that converts a raw address string into a sequence of numeric token IDs the model can process. Mailwoman's tokenizer is a SentencePiece unigram model trained specifically on postal addresses. can handle both scripts, but the modelneural classifierThe machine learning model at the core of Mailwoman's parser — a transformer encoder (~30M parameters) trained from scratch to do BIO token classification over addresses. It learns the 'grammar' of address formats; the gazetteer supplies the 'atlas.''s per-tokentokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words. embeddingsembeddingA vector of numbers representing a token (or other item) so that similar items sit near each other in vector space. The first thing the model does is turn each token into an embedding. may not have strong representationshidden stateThe model's internal vector for a token after the encoder has mixed in surrounding context. The contextualized representation the classifier head reads to assign a label. for tokenstokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words. in scripts that are sparse in the trainingtrainingThe process of adjusting a model's parameters so its predictions match labeled examples, by repeatedly measuring error and nudging the weights to reduce it. Distinct from inference, when the trained model is run on new input. corpuscorpusThe BIO-labeled training data used to train Mailwoman's neural classifier. Assembled from real sources (OpenAddresses, National Address Database) and synthetic shards (boundary stress, order variants, negative space). Managed by @mailwoman/corpus..
  • Character encoding recovery. A parcelparcelA property polygon or record carrying a situs (site) address and often a separate owner mailing address. County GIS parcel aggregations are a training source for address-point variety and situs-vs-owner divergence. labelcomponent tagOne of the 33 labels in Mailwoman's address schema — street, locality, region, postcode, house_number, unit, po_box, country, venue, intersection, and others. Each parsed span carries exactly one component tag. where Cyrillic was mangled by the wrong character encoding (latin1 instead of UTF-8) produces garbage text. A Russian postal worker might reverse the mapping and deliver the parcelparcelA property polygon or record carrying a situs (site) address and often a separate owner mailing address. County GIS parcel aggregations are a training source for address-point variety and situs-vs-owner divergence.. A parser cannot — this is a pre-parsingaddress parsingThe process of decomposing a free-text postal address string into structured components — house number, street name, locality, region, postcode, and country — so a geocoder can resolve them to coordinates. input-quality problem.
  • Addresses that have changed since the corpuscorpusThe BIO-labeled training data used to train Mailwoman's neural classifier. Assembled from real sources (OpenAddresses, National Address Database) and synthetic shards (boundary stress, order variants, negative space). Managed by @mailwoman/corpus. was built. The modelneural classifierThe machine learning model at the core of Mailwoman's parser — a transformer encoder (~30M parameters) trained from scratch to do BIO token classification over addresses. It learns the 'grammar' of address formats; the gazetteer supplies the 'atlas.' learns from the trainingtrainingThe process of adjusting a model's parameters so its predictions match labeled examples, by repeatedly measuring error and nudging the weights to reduce it. Distinct from inference, when the trained model is run on new input. data's address formats. If an administrative regionregionThe first-level administrative subdivision of a country — a US state, a French region, a province. The component between country and locality. was renamed after the corpuscorpusThe BIO-labeled training data used to train Mailwoman's neural classifier. Assembled from real sources (OpenAddresses, National Address Database) and synthetic shards (boundary stress, order variants, negative space). Managed by @mailwoman/corpus. freeze, the modelneural classifierThe machine learning model at the core of Mailwoman's parser — a transformer encoder (~30M parameters) trained from scratch to do BIO token classification over addresses. It learns the 'grammar' of address formats; the gazetteer supplies the 'atlas.' will not recognize the new name — it will classify it based on structural featuresfeatureAn input signal a model conditions on. Beyond the raw tokens, Mailwoman feeds soft features — gazetteer-membership channels and the postcode anchor — that inform predictions without overriding them. (position, surrounding tokenstokenOne word or subword in the tokenized input. For the neural classifier, tokens come from SentencePiece (subword units); for the rule classifiers, tokens are whitespace- and punctuation-separated words.) rather than semantic knowledge. This is acceptable because the modelneural classifierThe machine learning model at the core of Mailwoman's parser — a transformer encoder (~30M parameters) trained from scratch to do BIO token classification over addresses. It learns the 'grammar' of address formats; the gazetteer supplies the 'atlas.''s job is structural parsingaddress parsingThe process of decomposing a free-text postal address string into structured components — house number, street name, locality, region, postcode, and country — so a geocoder can resolve them to coordinates., not gazetteergazetteerA geographical index that maps place names and postcodes to real-world coordinates. Mailwoman uses a custom-built Who's On First (WOF) SQLite database as its gazetteer — the 'atlas' half of the grammar/atlas architecture. lookup.

References​