Triple

T236391
Position Surface form Disambiguated ID Type / Status
Subject Silesia E4833 entity
Predicate hasSubregion P285 FINISHED
Object Cieszyn Silesia
Cieszyn Silesia is a historical and ethnically diverse borderland region centered around the city of Cieszyn, spanning areas of present-day Poland and the Czech Republic.
 E31409 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: Cieszyn Silesia | Statement: [Silesia, hasSubregion, Cieszyn Silesia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cieszyn Silesia
Context triple: [Silesia, hasSubregion, Cieszyn Silesia]
  • A. Wrocław
    Wrocław is a major historic city in southwestern Poland, known for its picturesque Old Town, numerous bridges over the Oder River, and role as a cultural and academic center.
  • B. Tarnów
    Tarnów is a historic city in southern Poland known for its well-preserved Old Town, Renaissance architecture, and cultural heritage.
  • C. Chrzanów
    Chrzanów is a town in southern Poland known for its historical architecture and role as a local industrial and administrative center.
  • D. Glogów
    Glogów is a historic town in western Poland on the Oder River, known for its medieval origins and reconstructed Old Town.
  • E. Łódź
    Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
  • 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: Cieszyn Silesia
Triple: [Silesia, hasSubregion, Cieszyn Silesia]
Generated description
Cieszyn Silesia is a historical and ethnically diverse borderland region centered around the city of Cieszyn, spanning areas of present-day Poland and the Czech Republic.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cieszyn Silesia
Target entity description: Cieszyn Silesia is a historical and ethnically diverse borderland region centered around the city of Cieszyn, spanning areas of present-day Poland and the Czech Republic.
  • A. Wrocław
    Wrocław is a major historic city in southwestern Poland, known for its picturesque Old Town, numerous bridges over the Oder River, and role as a cultural and academic center.
  • B. Tarnów
    Tarnów is a historic city in southern Poland known for its well-preserved Old Town, Renaissance architecture, and cultural heritage.
  • C. Chrzanów
    Chrzanów is a town in southern Poland known for its historical architecture and role as a local industrial and administrative center.
  • D. Glogów
    Glogów is a historic town in western Poland on the Oder River, known for its medieval origins and reconstructed Old Town.
  • E. Łódź
    Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
  • 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_69a257c3d0708190b0871c4269d273e6 completed Feb. 28, 2026, 2:49 a.m.
NER Named-entity recognition batch_69a25ccab7648190be6e4f5febc1e313 completed Feb. 28, 2026, 3:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3695ec8cc8190a070462cd0022f6a completed Feb. 28, 2026, 10:17 p.m.
NEDg Description generation batch_69a36a08408c8190af33b6d33000b78e completed Feb. 28, 2026, 10:19 p.m.
NED2 Entity disambiguation (via description) batch_69a36a57f5048190ab4e96090a310977 completed Feb. 28, 2026, 10:21 p.m.
Created at: Feb. 28, 2026, 2:53 a.m.