Name Normalization

Election data arrives with candidate names in dozens of formats. MEDSL uses LAST, FIRST MIDDLE in all caps. NC SBE uses First Last in title case. OpenElections uses whatever the county clerk typed. FEC uses LAST, FIRST MIDDLE SUFFIX. A single candidate can appear as:

• CRIST, CHARLES JOSEPH (MEDSL)
• Charlie Crist (OpenElections)
• Crist, Charlie (FEC)

These are all the same person. A system that treats them as three different candidates produces garbage output. A system that aggressively normalizes them — stripping middle names, collapsing nicknames, removing suffixes — destroys the signal needed to tell different people apart.

The principle: clean without collapsing.

Name decomposition at L1

Every candidate name is decomposed at L1 into six components:

The canonical_first field is populated by the nickname dictionary. If the raw first name is Charlie, canonical_first becomes Charles. If no mapping exists, canonical_first equals first.

Both first and canonical_first are preserved. The raw nickname is useful signal — it tells you what the candidate goes by. The canonical form is what enables matching.

Real decomposition examples

Five candidates from our prototype, showing how MEDSL and NC SBE formats decompose differently for the same people:

Key observations from these examples:

1. Ron DeSantis — Ron maps to Ronald via the nickname dictionary. The embedding score between the two source representations is 0.729 — below any reasonable auto-accept threshold, but the LLM matches them using nickname knowledge.

2. Charlie Crist — Charlie maps to Charles. The embedding score is 0.451. Without the dictionary, the cascade would need the LLM to know that Charlie is a nickname for Charles. With the dictionary, the canonical forms already match.

3. Robert Williams vs Robert Williams Jr — The suffix Jr is the only distinguishing feature. These are different people. The embedding scores them at 0.862 — dangerously close to a false positive. See Suffixes.

4. David S Marshall vs David A Marshall — Different middle initials. David S. Marshall ran in Maine; David A. Marshall ran in Florida. The middle initial is the only signal distinguishing them at the name level. See Nicknames and Middle Initials.

What decomposition enables

With names decomposed into components, downstream layers can:

• Exact-match on structured fields: (canonical_first="Timothy", last="Lance", suffix=null) matches across precincts without fuzzy logic. This handles 70% of entity resolution.
• Build composite strings for embedding: "{canonical_first} {middle} {last} {suffix} | {party} | {office} | {state}" includes middle initials and suffixes as disambiguation signal.
• Provide structured context to the LLM: Instead of asking “are these the same person?”, the LLM sees parsed components and can reason about specific differences (nickname vs. different name, Jr vs. no suffix).
• Block efficiently: Group by (state, last_name_initial) for entity resolution without computing all-pairs similarity.

What goes wrong without decomposition

If you treat names as opaque strings:

• CRIST, CHARLES JOSEPH and Charlie Crist have a Jaro-Winkler similarity of 0.58 — a miss.
• DESANTIS, RON and Ron DeSantis have a cosine embedding similarity of 0.729 — in the ambiguous zone.
• Robert Williams and Robert Williams Jr look nearly identical to every string metric. Only structured suffix detection prevents a false merge.
• David S Marshall and David A Marshall differ by one character in a middle initial that opaque matching may ignore entirely.

Decomposition is not optional. It is the foundation that every subsequent layer depends on.

The three sub-problems

Name normalization breaks into three sub-problems, each with its own chapter:

1. Nicknames and Middle Initials — How Charlie becomes Charles and why David S. must stay distinct from David A.
2. Suffixes: Jr/Sr Means Different People — Why generational suffixes are disambiguation signals, not noise to be stripped.
3. The Nickname Dictionary — The lookup table that powers canonical_first, its current scope, and its limits.