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Klaret: Spisy, Dílo & Odkazy
Mistr Klaret, latinsky Magister Bohemarius Bartholomaeus de Solencia dictus Claretus, vlastním jménem Bartoloměj z Chlumce (asi 1320, Chlumec nad Cidlinou – 3. května 1370) byl český spisovatel, učenec, lékař a lexikograf, kanovník katedrály svatého Víta a mistr pražské univerzity.
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Klaret topic overview
The analysis highlights Spisy, Dílo and Odkazy as prominent areas in the source structure around Klaret.
Related topics
43
Source areas
5
Connected nodes
48
Extracted relationships
64
Concept neighborhoods
14
Bridge connections
48
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.
Spisy · 14 topics
Dílo · 11 topics
Overview · 8 topics
Odkazy · 7 topics
Život · 3 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.
Narození
1320 Chlumec nad Cidlinou
Povolání
spisovatel, pedagog, básník, lékař, kněz, učitel a lexikograf
Zaměstnavatel
Filozofická fakulta Univerzity Karlovy
Úmrtí
3. května 1370 (ve věku 49–50 let) Praha
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 Klaret 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 Klaret shows recurring relationship patterns in the source. For example, Klaret → Academia Praha, B-Bař, Biografický, Brno, Chlumce, Flajšhans, Fyziologické, Hádankář, II, In, ISBN, Jana Nechutová, Klaretových, Libri, Listy, Michálek, Mistr Klaret, Pavel Spunár, Pavla, Praha Another extracted example is Klaret → Arnošt, Bible, Břevnova, Havel, Jako, Jan, Jan Očko, Je, Jeho, Karel IV, Klaretovo, Látku, Mistr Jan Hus, Pardubic, Protože, Strahova, Vlašimi, Zhruba. Use these groups to spot repeated connection types before inspecting the individual relationships.
Klaret
Top relations
related to Literatura· 29
Klaret → Academia Praha, B-Bař, Biografický, Brno, Chlumce, Flajšhans, Fyziologické, Hádankář, II, In, ISBN, Jana Nechutová, Klaretových, Libri, Listy, Michálek, Mistr Klaret, Pavel Spunár, Pavla, Praha
related to Dílo· 18
Klaret → Arnošt, Bible, Břevnova, Havel, Jako, Jan, Jan Očko, Je, Jeho, Karel IV, Klaretovo, Látku, Mistr Jan Hus, Pardubic, Protože, Strahova, Vlašimi, Zhruba
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 64 structured relationships around Klaret. Examples in this analysis include Klaret → Narození → 1320 Chlumec nad Cidlinou and Klaret → Povolání → spisovatel, pedagog, básník, lékař, kněz, učitel a lexikograf. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Klaret
Narození
1320 Chlumec nad Cidlinou
1.00
infobox
Klaret
Povolání
spisovatel, pedagog, básník, lékař, kněz, učitel a lexikograf
1.00
infobox
Klaret
Zaměstnavatel
Filozofická fakulta Univerzity Karlovy
1.00
infobox
Klaret
Úmrtí
3. května 1370 (ve věku 49–50 let) Praha
1.00
infobox
Klaret
related to Dílo
Jeho
0.60
section
Klaret
related to Dílo
Je
0.60
section
Klaret
related to Dílo
Protože
0.60
section
Klaret
related to Dílo
Látku
0.60
section
Klaret
related to Dílo
Bible
0.60
section
Klaret
related to Dílo
Arnošt
0.60
section
Klaret
related to Dílo
Pardubic
0.60
section
Klaret
related to Dílo
Jan Očko
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 Klaret bring nearby vocabulary together. In this analysis, examples include Bartoloměj, Chlumce and Jeho. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
chlumec nad cidlinou
Cidlinou
Května
Lexikograf
Spisovatel
Lékař
Univerzity
Claretus
Datové
Latinsky
Literatura
Poezie
Položky
Klaret
Bartoloměj
Chlumce
Jeho
Mistr
Bohemarius
Chlumec
Cidlinou
Claretus
Května
Latinsky
Lexikograf
Pražské
klaret
Bartoloměj
Chlumce
Jeho
Mistr
Bohemarius
Chlumec
Cidlinou
Claretus
Května
Latinsky
Lexikograf
Pražské
3. května
Lexikograf
Spisovatel
Lékař
Univerzity
Datové
Latinsky
Literatura
Poezie
Položky
Pražské
Solencia
Spisy
český překlad bible
Mistr
Bohemarius
Chlumec
Cidlinou
Claretus
Května
Latinsky
Lexikograf
Pražské
Solencia
Spisovatel
Víta
lexikograf
Spisovatel
Lékař
Univerzity
Datové
Literatura
Poezie
Položky
Pražské
Solencia
Spisy
Víta
Vědecké
mistr jan hus
Český
Chlumec
Cidlinou
Claretus
Května
Lexikograf
Pražské
Solencia
Spisovatel
Spisy
Víta
Lékař
dílo
Slovníky
Datové
Května
Lexikograf
Literatura
Poezie
Položky
Spisovatel
Spisy
Vědecké
Lékař
Univerzity
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 Klaret, one of the stronger structural bridges in this analysis connects Klaret with Spisy. 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.
Klaret
Nodes49
Edges48
Triples64
Avg. degree1.96
Density0.040816
Components1
Source & methodology
TTTA analyzes the structure around Klaret to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Spisy, Dílo & Odkazy, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
