Triple

T15539133
Position Surface form Disambiguated ID Type / Status
Subject E370428 entity
Predicate hasAdministrativePart P3892 FINISHED
Object Vernéřov
Vernéřov is a small village that forms one of the administrative parts of the town of Aš in the Karlovy Vary Region of the Czech Republic.
E1162187 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: Vernéřov | Statement: [Aš, hasAdministrativePart, Vernéřov]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vernéřov
Context triple: [Aš, hasAdministrativePart, Vernéřov]
  • A. Venyov
    Venyov is a historic town in Tula Oblast, Russia, known for its role as a local administrative and cultural center.
  • B. Novotný
    Novotný is a common Czech surname borne by various notable figures in politics, arts, and sports.
  • C. Beránek
    Beránek is a Czech surname and word meaning "little lamb," commonly used as a family name in Czech-speaking regions.
  • D. Viotá
    Viotá is a rural municipality in the Cundinamarca department of Colombia, known for its coffee production and location in the Andean region southwest of Bogotá.
  • E. Vamberk
    Vamberk is a Czech town in the Hradec Králové Region renowned for its long tradition of handmade bobbin lace-making.
  • 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: Vernéřov
Triple: [Aš, hasAdministrativePart, Vernéřov]
Generated description
Vernéřov is a small village that forms one of the administrative parts of the town of Aš in the Karlovy Vary Region of the Czech Republic.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Vernéřov
Target entity description: Vernéřov is a small village that forms one of the administrative parts of the town of Aš in the Karlovy Vary Region of the Czech Republic.
  • A. Venyov
    Venyov is a historic town in Tula Oblast, Russia, known for its role as a local administrative and cultural center.
  • B. Novotný
    Novotný is a common Czech surname borne by various notable figures in politics, arts, and sports.
  • C. Beránek
    Beránek is a Czech surname and word meaning "little lamb," commonly used as a family name in Czech-speaking regions.
  • D. Viotá
    Viotá is a rural municipality in the Cundinamarca department of Colombia, known for its coffee production and location in the Andean region southwest of Bogotá.
  • E. Vamberk
    Vamberk is a Czech town in the Hradec Králové Region renowned for its long tradition of handmade bobbin lace-making.
  • 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_69d85cc521a08190921fb50319dddc34 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04430b5188190a555a3cd4fb0c61c completed April 16, 2026, 2:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3d626e688190bd93481cfd6cb255 completed May 9, 2026, 1:57 p.m.
NEDg Description generation batch_69ff3f075bb881908c254137ca7c3f9f completed May 9, 2026, 2:04 p.m.
NED2 Entity disambiguation (via description) batch_69ff3f87f788819080eccae52b0df145 completed May 9, 2026, 2:07 p.m.
Created at: April 10, 2026, 4:07 a.m.