Triple

T9418121
Position Surface form Disambiguated ID Type / Status
Subject Žilina Region E227080 entity
Predicate contains P35 FINISHED
Object Tvrdošín
Tvrdošín is a town in northern Slovakia known for its historic wooden church and location near the Orava region’s natural attractions.
 E799321 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tvrdošín | Statement: [Žilina Region, contains, Tvrdošín]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tvrdošín
Context triple: [Žilina Region, contains, Tvrdošín]
  • A. Dobrošov
    Dobrošov is a small village in the Hradec Králové Region of the Czech Republic, known for its scenic location near Náchod and its historic World War II fortifications.
  • B. Štrkovec
    Štrkovec is a residential neighborhood and cadastral area within the Ružinov borough of Bratislava, Slovakia.
  • C. Vrchlabí
    Vrchlabí is a Czech town in the northern Bohemian foothills of the Krkonoše (Giant) Mountains, known as a gateway to the nearby ski and mountain resort areas.
  • D. Vávrová
    Vávrová is a Czech surname most notably borne by Dana Vávrová, a well-known Czech-German actress and film director.
  • E. Kriváň
    Kriváň is a prominent and symbolically important peak in Slovakia’s High Tatras, often regarded as a national symbol and popular hiking destination.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tvrdošín
Triple: [Žilina Region, contains, Tvrdošín]
Generated description
Tvrdošín is a town in northern Slovakia known for its historic wooden church and location near the Orava region’s natural attractions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tvrdošín
Target entity description: Tvrdošín is a town in northern Slovakia known for its historic wooden church and location near the Orava region’s natural attractions.
  • A. Dobrošov
    Dobrošov is a small village in the Hradec Králové Region of the Czech Republic, known for its scenic location near Náchod and its historic World War II fortifications.
  • B. Štrkovec
    Štrkovec is a residential neighborhood and cadastral area within the Ružinov borough of Bratislava, Slovakia.
  • C. Vrchlabí
    Vrchlabí is a Czech town in the northern Bohemian foothills of the Krkonoše (Giant) Mountains, known as a gateway to the nearby ski and mountain resort areas.
  • D. Vávrová
    Vávrová is a Czech surname most notably borne by Dana Vávrová, a well-known Czech-German actress and film director.
  • E. Kriváň
    Kriváň is a prominent and symbolically important peak in Slovakia’s High Tatras, often regarded as a national symbol and popular hiking destination.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca84359e7c819091148ba4b670e436 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd68cd1e3481909abcb715e2398120 completed April 1, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69d11029d3348190baf0dba766c4e960 completed April 4, 2026, 1:20 p.m.
NEDg Description generation batch_69d111113c5c81909ff654734b211753 completed April 4, 2026, 1:24 p.m.
NED2 Entity disambiguation (via description) batch_69d111ab40a48190bb77c1cf80ef87a8 completed April 4, 2026, 1:27 p.m.
Created at: March 30, 2026, 7:48 p.m.