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Srpen je podle gregoriánského kalendáře osmý měsíc v roce. Má 31 dní. Český název pochází pravděpodobně od zemědělského nástroje srpu, užívaného při sklizni obilí. V římském kalendáři měl měsíc název Sextilis od latinského sextus (tj. šestý), neboť do roku 153 př. n. l. byl srpen šestým měsícem v roce. Když Julius Caesar v roce 46 př. n. l. zavedl…
The analysis highlights Overview, Related Topics and Entities as prominent areas in the source structure around Srpen.
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.
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.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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.
The extracted context around Srpen shows recurring relationship patterns in the source. For example, Srpen → Bartoloměj, Co, Hřímá-li, Jak Vavřinec, Jsou-li, Když, Mlhy, Moc, Nejsou-li, Půlnoční, Rosí-li, Teplé Another extracted example is Srpen → Moderní, Obrázky, Wikimedia Commons Slovníkové, WikislovníkuSrpen. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
srpna srpnu den měsíc roce př letech 20 slunce měsíce ze 31 dní název dnech jeden české hodin teplota klementinu
TTTA extracted 16 structured relationships around Srpen. Examples in this analysis include Srpen → related to Externí odkazy → Obrázky and Srpen → related to Externí odkazy → Wikimedia Commons Slovníkové. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Srpen | related to Externí odkazy | Obrázky | 0.60 | section |
| Srpen | related to Externí odkazy | Wikimedia Commons Slovníkové | 0.60 | section |
| Srpen | related to Externí odkazy | WikislovníkuSrpen | 0.60 | section |
| Srpen | related to Externí odkazy | Moderní | 0.60 | section |
| Srpen | related to Pranostiky | Co | 0.60 | section |
| Srpen | related to Pranostiky | Půlnoční | 0.60 | section |
| Srpen | related to Pranostiky | Když | 0.60 | section |
| Srpen | related to Pranostiky | Rosí-li | 0.60 | section |
| Srpen | related to Pranostiky | Hřímá-li | 0.60 | section |
| Srpen | related to Pranostiky | Mlhy | 0.60 | section |
| Srpen | related to Pranostiky | Teplé | 0.60 | section |
| Srpen | related to Pranostiky | Moc | 0.60 | section |
The concept neighborhoods around Srpen bring nearby vocabulary together. In this analysis, examples include Připadá, Stejným and Týdnu. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Bridges highlight paths between different parts of the Srpen map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Srpen to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Overview, Related Topics & Entities, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Srpen · CS edition · Analysis: TopicsToTalkAbout