The Italian lawmaking process: a unified and structured dataset
Authors: Matilde Contestabile, Chiara Ferrara, Giovanni Parrillo, Lorenzo Emer, Andrea Vandin
Organizations: Institute of Economics and L’EMbeDS, Scuola Superiore Sant’Anna, Piazza Martiri della Libert`a, 33, Pisa, 56127, Italy. · Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, Pisa, 56126, Italy. · DTU Compute, Technical University of Denmark, Anker Engelunds Vej 101, Kongens Lyngby, 2800, Denmark.
The Italian legislative process is documented in detailed parliamentary records hosted in institutional websites. However, much of this information is available only as unstructured text, limiting its systematic reuse. We applied a controlled, systematic LLM-based extraction procedure, validated against manually annotated records, to convert these texts into ordered and dated events including, e.g., bill introduction, committee examination, plenary examination, and approval. The resulting dataset reconstructs the procedural histories of all 5346 Italian laws enacted between 1985 and 2026, spanning parliamentary terms IX to XIX, for overall 101413 events. Each event records the activity, date, and chamber, with additional procedural details where available. The dataset supports research in the emerging fields of computational legal and legislative studies, potentially combined with process-oriented data science methods to examine procedural paths and legislative timing across parliamentary terms.
Bill severance ( stralcio ), drafting procedure ( redigente ), and bicameral transfers.
26G00137
Leg. XIX
14
High committee density ( >10 bodies), repeated hearings, and bicameral passage.
Table 2: Overview of the three examples inserted in the prompt (few-shot prompting), including legislature, number of events, and elements providing a rationale for the choice of the examples.
Parameter
Value
Operational Rationale
Model
Gemma 4 (31B)
Open-weight instruction-tuned LLM
Hardware
NVIDIA H200 (141 GB)
Local execution, full precision
Serving backend
Ollama (JSON mode)
Grammar-constrained decoding for schema validity
Decoding temp.
0.0
Greedy decoding for deterministic repeatability
Random seed
42
Fixed seed for exact scientific reproducibility
Reasoning / CoT
Disabled ( think: false )
Prevents JSON syntax breakage and context bloat
Table 3: Inference environment, hardware specifications, and decoding hyperparameters.
Variable
Type
Availability
Description
law_id
String
5 346 (100.0%)
Official Gazette identifier (e.g., "26G00136" ), serving as primary key and join key to the procedural event log.
law_number
String
5 346 (100.0%)
Official statutory law number (e.g., "129/2004" , "137/2026" ).
legislature
Integer
5 346 (100.0%)
Parliamentary legislature index in Arabic numerals (9 to 19).
promulgation_date
String
3 091 (57.8%)
Official presidential enactment date in ISO 8601 format ( YYYY-MM-DD ).
num_articles
Integer
5 346 (100.0%)
Total count of statutory articles composing the enacted statute.
initiative
String
2 202 (41.2%)
Institutional origin ( Government , Parliamentary , Regional Council , Popular ).
Table 4: Data dictionary for the macro-level Laws Registry ( laws_registry.csv ).
Model
Params
Zero-shot (%)
Few-shot (%)
Gemma 4
31B
99.95
99.95
Gemma 4
12B
99.85
100.00
Gemma 3
27B
98.95
100.00
Qwen 3.5
27B
100.00
100.00
Qwen 3.5
9B
94.55
99.25
Qwen 3.6
35B
100.00
100.00
Table 5: Schema compliance across the eight evaluated models under zero-shot and few-shot prompting. Values indicate the reported percentage of outputs conforming to the required JSON schema.
Model
Params
Zero-shot (%)
Few-shot (%)
Gemma 4
31B
87.28
92.73
Gemma 4
12B
79.53
86.30
Gemma 3
27B
62.41
89.05
Qwen 3.5
27B
80.00
91.29
Qwen 3.5
9B
25.96
72.73
Qwen 3.6
35B
67.41
88.90
Table 6: Exact-match event-set Jaccard similarity across the eight evaluated models. Values are percentages of the reported Jaccard scores, with an event considered a match only when all ten attributes agree.
Zero-shot
Few-shot
Model
Params
Micro F1
Macro F1
Micro F1
Macro F1
Gemma 4
31B
99.75
99.77
99.80
99.87
Gemma 4
12B
99.53
99.43
99.28
98.82
Gemma 3
27B
96.34
98.41
98.23
99.18
Qwen 3.5
27B
99.58
99.73
99.73
99.74
Qwen 3.5
9B
94.11
96.56
97.26
97.26
Table 7: Similarity-based event matching performance (micro and macro F1 , in percent) under zero-shot and few-shot prompting. Events were paired using the Hungarian algorithm with a cosine similarity threshold of τ=0.80 .
Systems and Software Lab (SSL) · Department of Computer Science and Engineering · Department of Computer Science and Engineering Islamic University of Technology, Dhaka, Bangladesh