Under the Whyte notation for the classification of steam locomotives, 2-2-2 represents the wheel arrangement of two leading wheels on one axle, two powered driving wheels on one axle, and two trailing wheels on one axle. The wheel arrangement both provided more stability and enabled a larger firebox than the earlier 0-2-2 and 2-2-0 types. This wheel…
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2-2-2 topic overview
The analysis highlights History, Measurement, Standards and Companies as prominent areas in the source structure around 2-2-2.
Related topics
83
Source areas
3
Connected nodes
86
Extracted relationships
95
Concept neighborhoods
42
Bridge connections
86
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
History · 68 topics
Overview · 10 topics
Equivalent classifications · 5 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How 2-2-2 connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around 2-2-2 shows recurring relationship patterns in the source. For example, 2-2-2 → Alexander Allan's, Arend, Bedlington, Brighton Railway, Bristol, Bury, Columbine, Company, Cork Kent, Croydon Railway, Curtis, Firefly, Gloucester Railway, Grand Junction Railway, Great Southern, Ireland, John Rennie, Kennedy, London, Longridge Another extracted example is 2-2-2 → Adler, Also, By, Charles Tayleur, Company, December, Eighteen, Germany, Great Western Railway, Isambard Kingdom Brunel, Italy, Netherlands, North Star, Other, Patentee, Planet, Robert Stephenson, Russia, Stephenson, Stephenson's Patentee. Use these groups to spot repeated connection types before inspecting the individual relationships.
2-2-2
Top relations
related to Later UK developments· 29
2-2-2 → Alexander Allan's, Arend, Bedlington, Brighton Railway, Bristol, Bury, Columbine, Company, Cork Kent, Croydon Railway, Curtis, Firefly, Gloucester Railway, Grand Junction Railway, Great Southern, Ireland, John Rennie, Kennedy, London, Longridge
related to history· 23
2-2-2 → Adler, Also, By, Charles Tayleur, Company, December, Eighteen, Germany, Great Western Railway, Isambard Kingdom Brunel, Italy, Netherlands, North Star, Other, Patentee, Planet, Robert Stephenson, Russia, Stephenson, Stephenson's Patentee
related to Germany· 22
2-2-2 → August Borsig, Bavarian, Beuth, Both, By, Chemnitz, Company, German, Germany, Grand Duchy, However, Königlich Bayerische Staatsbahn, Leipzig-Dresden Railway, List, Mecklenburg, Mecklenburg Friedrich-Franz Railway, Most, Royal Bavarian State Railways, Similarly, Sächsische Maschinenbau-Compagnie
related to Latvia· 12
2-2-2 → After World War II, German Hohenzollern, Germany, Latvia, Latvian Railways, OKa1, One, Poland, Soviet, The, They, Tk
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 95 structured relationships around 2-2-2. Examples in this analysis include 2-2-2 → related to Germany → Most and 2-2-2 → related to Germany → Germany. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
2-2-2
related to Germany
Most
0.60
section
2-2-2
related to Germany
Germany
0.60
section
2-2-2
related to Germany
UK
0.60
section
2-2-2
related to Germany
However
0.60
section
2-2-2
related to Germany
List
0.60
section
2-2-2
related to Germany
Bavarian
0.60
section
2-2-2
related to Germany
The Pegasus
0.60
section
2-2-2
related to Germany
Sächsische Maschinenbau-Compagnie
0.60
section
2-2-2
related to Germany
Chemnitz
0.60
section
2-2-2
related to Germany
August Borsig
0.60
section
2-2-2
related to Germany
Company
0.60
section
2-2-2
related to Germany
Beuth
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around 2-2-2 bring nearby vocabulary together. In this analysis, examples include Locomotives, Railway and Steam. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
2-2-2
Locomotives
Railway
Steam
Great
Western
Company
Built
German
London
Preserved
Robert
Supplied
2-2-2
Locomotives
Railway
Steam
Great
Western
Company
Built
German
London
Preserved
Robert
Supplied
steam locomotives
2-2-2
One
Two
Railway
Built
First
Great
Western
Driving
Wheels
Belgium
Class
robert stephenson & company
Stephenson
Company
Robert
Design
First
Successful
Standard
Uk
Became
Germany
Examples
North
patentee locomotive
First
Stephenson
Railway
Built
London
North
Robert
Examples
Successful
Western
Company
Wheels
great western railway
Great
Western
North
Railway
Class
Singles
Locomotives
First
London
2-2-2
Supplied
Built
2-2-2 locomotives
2-2-2
Locomotives
Railway
Two
Built
Great
Western
Steam
Company
Class
One
Railways
great southern and western railway
Great
Western
North
Railway
Class
Singles
Locomotives
First
London
2-2-2
Supplied
Built
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For 2-2-2, one of the stronger structural bridges in this analysis connects 2-2-2 with History. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
2-2-2
Nodes87
Edges86
Triples95
Avg. degree1.98
Density0.022989
Components1
Source & methodology
TTTA analyzes the structure around 2-2-2 to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Measurement, Standards & Companies, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — 2-2-2 · EN edition · Analysis: TopicsToTalkAbout
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