Triple

T14774219
Position Surface form Disambiguated ID Type / Status
Subject Skierniewice E347212 entity
Predicate hasTwinTown P919 FINISHED
Object Võru
Võru is a small town in southeastern Estonia known for its lakeside setting, traditional Võro culture, and role as a regional administrative and cultural center.
 E1121202 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: Võru | Statement: [Skierniewice, hasTwinTown, Võru]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Võru
Context triple: [Skierniewice, hasTwinTown, Võru]
  • A. Jõhvi
    Jõhvi is a town in northeastern Estonia that serves as the administrative center of Ida-Viru County.
  • B. Põlva
    Põlva is a small town in southeastern Estonia known as a local administrative and cultural center surrounded by lakes and forested landscapes.
  • C. Haapsalu
    Haapsalu is a small seaside town in western Estonia known for its historic wooden architecture, medieval castle, and traditional seaside resort and spa culture.
  • D. Kohtla-Järve
    Kohtla-Järve is an industrial city in northeastern Estonia known for its oil shale industry and diverse population.
  • E. Jõgeva
    Jõgeva is a small town in eastern Estonia known as a local administrative and cultural center and for recording some of the country’s lowest winter temperatures.
  • 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: Võru
Triple: [Skierniewice, hasTwinTown, Võru]
Generated description
Võru is a small town in southeastern Estonia known for its lakeside setting, traditional Võro culture, and role as a regional administrative and cultural center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Võru
Target entity description: Võru is a small town in southeastern Estonia known for its lakeside setting, traditional Võro culture, and role as a regional administrative and cultural center.
  • A. Jõhvi
    Jõhvi is a town in northeastern Estonia that serves as the administrative center of Ida-Viru County.
  • B. Põlva
    Põlva is a small town in southeastern Estonia known as a local administrative and cultural center surrounded by lakes and forested landscapes.
  • C. Haapsalu
    Haapsalu is a small seaside town in western Estonia known for its historic wooden architecture, medieval castle, and traditional seaside resort and spa culture.
  • D. Kohtla-Järve
    Kohtla-Järve is an industrial city in northeastern Estonia known for its oil shale industry and diverse population.
  • E. Jõgeva
    Jõgeva is a small town in eastern Estonia known as a local administrative and cultural center and for recording some of the country’s lowest winter temperatures.
  • 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_69d822e9b9e08190bedcc31a163fda82 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec815dd5081909e927911c06b2d66 completed April 14, 2026, 11:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe24b41df881908119e183b8299c48 completed May 8, 2026, 6 p.m.
NEDg Description generation batch_69fe276bff648190a6be5d25182accd5 completed May 8, 2026, 6:11 p.m.
NED2 Entity disambiguation (via description) batch_69fe27c3fce48190914e2a84b1de527c completed May 8, 2026, 6:13 p.m.
Created at: April 10, 2026, 1:31 a.m.