A fine-tuned LoRA adapter for authority file linking in historical TEI editions. Given an entity from a TEI register (person, place, organization) and a list of candidates from Wikidata/GND, the model decides whether a candidate matches, partially matches, or does not match.
Task
The model solves a core problem in digital scholarly editing: linking named entities in historical registers to authority files (GND, Wikidata). This requires:
Disambiguating entities by biographical data, descriptions, and context
Low temperature for deterministic JSON output. Higher values cause malformed JSON.
top_p
0.9
Nucleus sampling, slightly narrowed
max_tokens
2048
Sufficient for batches of 5 entities. Scale up for larger batches.
repetition_penalty
1.0
No penalty needed — the model was trained on structured output
stop
] (optional)
Can be used to stop after the JSON array closes
The model uses Qwen3's built-in thinking mode (<think>...</think>). With these parameters, the thinking block is typically empty and the model responds directly with JSON. If you want to explicitly disable thinking, set enable_thinking: false in the chat template or prepend <think>\n\n</think>\n\n to the assistant message.
Send a request
The model expects an OpenAI-compatible chat format with a system prompt defining the task and a user prompt listing entities with candidates:
bash
1curl http://localhost:1234/v1/chat/completions \2 -H "Content-Type: application/json"\3 -d '{
4 "model": "Qwen/Qwen3-14B-MLX-4bit",
5 "temperature": 0.1,
6 "messages": [
7 {
8 "role": "system",
9 "content": "Du bist ein Normdaten-Experte fuer historische TEI-Editionen.\nEntscheide ob Normdaten-Kandidaten (Wikidata/GND) zu Entities aus historischen Registern passen.\n\nRegeln:\n- MATCH: >=90% sicher. PARTIAL: 75-90%. NONE: kein Kandidat passt.\n- Lebensdaten und Register-Notizen sind die staerksten Indikatoren.\n- Abstammung/Verwandtschaft MUSS uebereinstimmen.\n- Antworte NUR als JSON-Array.\n\nFormat pro Entity:\n{\"idx\": 1, \"verdict\": \"MATCH\"|\"PARTIAL\"|\"NONE\", \"best\": 1, \"confidence\": 0.95, \"reason\": \"Max 1 Satz\", \"modern_name\": null}"
10 },
11 {
12 "role": "user",
13 "content": "--- Entity 1 ---\nName: Philipp Melanchthon\nTyp: person\nNote: nachantik/fruehneuzeitlich; Lebensdaten: 1497-1560; Reformator und Humanist\n\nKandidaten:\n 1. GND 118580485 \"Melanchthon, Philipp\" — Theologe, Humanist, Reformator; 1497-1560\n 2. Wikidata Q76325 \"Philipp Melanchthon\" — German Protestant reformer (1497-1560)\n 3. GND 1089928572 \"Melanchthon, Philipp\" — Sohn des vorigen"
14 }
15 ]
16}'
Response
json
1[2{3"idx":1,4"verdict":"MATCH",5"best":1,6"confidence":0.99,7"reason":"Lebensdaten (1497-1560) und Beruf (Reformator, Humanist) stimmen ueberein.",8"modern_name":null9}10]
Input Format
Entity block structure
Each entity in the user prompt follows this structure:
Candidates are listed with their source ([wikidata] or [gnd]), a label, description, and an optional pre-computed similarity score. The authority file ID (Wikidata Q-ID or GND number) is passed on a separate ID: line below each candidate.
Important: The model was trained on single-entity prompts (one entity per request). It also works well with batches of 2-5 entities per call, but single-entity prompts match the training distribution most closely.
Entity fields
Field
Required
Description
Name
yes
Entity name as it appears in the register
Typ
yes
person, place, or org. Subtypes can be annotated: person (PERSONENGRUPPE/VOLK), person (KOERPERSCHAFT)
Alternativnamen
no
Alternative name forms (Greek, Latin, historical spellings)
Lebensdaten (Register)
no
Birth/death dates from the register
Amt/Beruf (Register)
no
Occupation or title from the register
Register-Notiz
no
Free-text note with biographical/geographical context. This is the strongest disambiguation signal.
Quelltext-Kontext
no
Surrounding text from the TEI source document
Kandidaten
yes
Numbered list of candidates with source, label, description, score, and ID
Candidate fields
Field
Required
Example
Source tag
yes
[wikidata] or [gnd]
Label
yes
Melanchthon, Philipp
Description
no
Theologe, Humanist, Reformator; 1497-1560
Life dates
no
(geb. 1497, gest. 1560) — appended after description
Score
no
(Score 0.77) — pre-computed similarity score
ID line
yes
ID: Q76325 or ID: 118580485 on the next line
If no candidates were found, use: (Keine Kandidaten gefunden)
Prompt header
The user prompt should start with a verification instruction, optionally including project-specific context:
Verifiziere diese Zuordnungen.
WICHTIG: KEINE zeitliche Filterung durchfuehren!
Pruefe NUR ob der Kandidat zur Register-Note passt.
Antike, mittelalterliche und biblische Figuren sind gueltig.
Prüfe auf historische Schreibweisen!
--- Entity 1 ---
...
Antwort als JSON-Array:
Output Format
JSON array with one object per entity:
Field
Type
Description
idx
int
Entity index (1-based)
verdict
string
MATCH (>=90%), PARTIAL (75-90%), NONE
best
int
1-based index of best candidate (0 if NONE)
confidence
float
0.0 - 1.0
reason
string
Brief justification (1 sentence)
modern_name
string|null
Modern spelling if register name is a historical variant (e.g. "Krefeld" for "Creveld")
Training
Method
The model was fine-tuned using LoRA (Low-Rank Adaptation) on Apple Silicon via MLX. Training data was generated through a teacher-student approach: Claude Sonnet 4 served as the verification oracle on real TEI register data, producing high-quality labeled examples. These were deduplicated, quality-filtered, and converted to ChatML format for supervised fine-tuning.
Each training example is a single entity with its candidates — the model learns to evaluate candidate quality based on name matching, biographical data, descriptions, and register context.
Historical TEI editions (early modern German correspondence, 16th century)
Classical philology registers (Greek/Latin named entities)
Enrichment pipeline output verified by Claude Sonnet 4
The data covers a wide range of entity types: historical persons with life dates, mythological/biblical figures, ethnic groups and peoples, geographic locations with historical spelling variants, and personifications/allegories.
Capabilities
Tested on representative entities from a 16th-century TEI edition:
Biographical disambiguation: Correctly distinguishes father/son with same name by life dates
Mythology vs. literature: Picks Q202990 (Hecuba the queen) over Q1421115 (Hecuba the play)
Ethnic groups/peoples: Matches "Trojaner" to GND 4060976-3 (Volk), not the city
Correct rejection: Returns NONE for underspecified entries like "Fridericus, Ilfelder Schüler"
Geographic disambiguation: Dillingen an der Donau vs. Dillingen/Saar, Wesel (NRW) vs. Wesel (Niedersachsen)
Integration
This adapter was built for the TEI-NER-Stack (GitHub repository TBA), an automated entity linking pipeline for historical TEI editions. The pipeline handles candidate retrieval (Wikidata/GND API), batching, confidence scoring, and XML output — the model serves as the verification step.
Requirements
Apple Silicon Mac (M1 or later) for MLX inference
~10 GB RAM for the 4-bit model + adapter
Python 3.10+, mlx-lm package
For GPU inference on other platforms, convert the adapter to Hugging Face PEFT format and use vLLM or text-generation-inference with the full-precision Qwen3-14B base model.
