Triple

T8580998
Position Surface form Disambiguated ID Type / Status
Subject Cottbus E203175 entity
Predicate twinnedWith P1072 FINISHED
Object Lipezk
Lipezk (often spelled Lipetsk) is an industrial city in western Russia known for its steel production and mineral springs.
 E919947 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: Lipezk | Statement: [Cottbus, twinnedWith, Lipezk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lipezk
Context triple: [Cottbus, twinnedWith, Lipezk]
  • A. Cieszyn
    Cieszyn is a historic town in southern Poland on the Olza River, known for its shared Polish-Czech heritage and well-preserved old town.
  • B. Łódź
    Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
  • C. Lublin
    Lublin is a historic city in eastern Poland known as a major cultural, academic, and economic center and for its significant role in Polish political history.
  • D. Mielec
    Mielec is a town in southeastern Poland known for its aviation industry and manufacturing sector.
  • E. 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.
  • 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: Lipezk
Triple: [Cottbus, twinnedWith, Lipezk]
Generated description
Lipezk (often spelled Lipetsk) is an industrial city in western Russia known for its steel production and mineral springs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lipezk
Target entity description: Lipezk (often spelled Lipetsk) is an industrial city in western Russia known for its steel production and mineral springs.
  • A. Cieszyn
    Cieszyn is a historic town in southern Poland on the Olza River, known for its shared Polish-Czech heritage and well-preserved old town.
  • B. Łódź
    Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
  • C. Lublin
    Lublin is a historic city in eastern Poland known as a major cultural, academic, and economic center and for its significant role in Polish political history.
  • D. Mielec
    Mielec is a town in southeastern Poland known for its aviation industry and manufacturing sector.
  • E. 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.
  • 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_69ca8329bb7c8190a63c643730839103 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbeb1a026c819089183f542eeb7837 completed March 31, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5424a26688190a49c3920d0edb546 completed April 19, 2026, 8:59 p.m.
NEDg Description generation batch_69e5474879088190990468d960b26739 completed April 19, 2026, 9:21 p.m.
NED2 Entity disambiguation (via description) batch_69e54eccdd3881908536ee3f9f4ef516 completed April 19, 2026, 9:53 p.m.
Created at: March 30, 2026, 6:22 p.m.