Triple

T8722648
Position Surface form Disambiguated ID Type / Status
Subject Ústí nad Labem Region E207048 entity
Predicate containsCity P294 FINISHED
Object Kadaň
Kadaň is a historic town in the northwestern Czech Republic, known for its well-preserved medieval center and location on the Ohře River.
 E752886 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: Kadaň | Statement: [Ústí nad Labem Region, containsCity, Kadaň]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kadaň
Context triple: [Ústí nad Labem Region, containsCity, Kadaň]
  • A. Terekhovo
    Terekhovo is a metro station on Moscow’s Big Circle Line, serving the Terekhovo area in the western part of the city.
  • B. Kirovakan
    Kirovakan is the former name of Vanadzor, a major industrial city in northern Armenia.
  • C. Kurskaya
    Kurskaya is a Moscow Metro station on the Koltsevaya (Circle) Line, serving as a major transfer hub in the city’s rapid transit network.
  • D. Kasimov
    Kasimov is a historic town in central Russia known for its Tatar heritage, medieval architecture, and location on the Oka River.
  • E. Kolomna
    Kolomna is a historic Russian city southeast of Moscow, known for its well-preserved kremlin, medieval architecture, and traditional pastila confectionery.
  • 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: Kadaň
Triple: [Ústí nad Labem Region, containsCity, Kadaň]
Generated description
Kadaň is a historic town in the northwestern Czech Republic, known for its well-preserved medieval center and location on the Ohře River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kadaň
Target entity description: Kadaň is a historic town in the northwestern Czech Republic, known for its well-preserved medieval center and location on the Ohře River.
  • A. Terekhovo
    Terekhovo is a metro station on Moscow’s Big Circle Line, serving the Terekhovo area in the western part of the city.
  • B. Kirovakan
    Kirovakan is the former name of Vanadzor, a major industrial city in northern Armenia.
  • C. Kurskaya
    Kurskaya is a Moscow Metro station on the Koltsevaya (Circle) Line, serving as a major transfer hub in the city’s rapid transit network.
  • D. Kasimov
    Kasimov is a historic town in central Russia known for its Tatar heritage, medieval architecture, and location on the Oka River.
  • E. Kolomna
    Kolomna is a historic Russian city southeast of Moscow, known for its well-preserved kremlin, medieval architecture, and traditional pastila confectionery.
  • 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_69ca835811d8819081ea00fd2a2c9a1c completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5d0609f48190adc56226724b16c6 completed March 31, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf290001108190a90784b13a0a25b1 completed April 3, 2026, 2:42 a.m.
NEDg Description generation batch_69cf2bd32cc881909ac8a61befa9929e completed April 3, 2026, 2:54 a.m.
NED2 Entity disambiguation (via description) batch_69cf2c69f83481909423858668d03a8b completed April 3, 2026, 2:56 a.m.
Created at: March 30, 2026, 6:36 p.m.