Triple

T1456310
Position Surface form Disambiguated ID Type / Status
Subject Lower Silesia E31408 entity
Predicate contains P35 FINISHED
Object Lubin
Lubin is a town in southwestern Poland known for its copper mining industry and location within the Lower Silesian region.
 E315898 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: Lubin | Statement: [Lower Silesia, contains, Lubin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lubin
Context triple: [Lower Silesia, contains, Lubin]
  • A. 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.
  • B. Tczew
    Tczew is a historic town in northern Poland on the Vistula River, known for its important railway bridges and role as a regional transport hub.
  • C. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • D. Wadowice
    Wadowice is a historic town in southern Poland best known as the birthplace of Pope John Paul II.
  • E. Jaworzno
    Jaworzno is a city in southern Poland, located in the Silesian Voivodeship and known for its industrial heritage and role in the Upper Silesian urban area.
  • 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: Lubin
Triple: [Lower Silesia, contains, Lubin]
Generated description
Lubin is a town in southwestern Poland known for its copper mining industry and location within the Lower Silesian region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lubin
Target entity description: Lubin is a town in southwestern Poland known for its copper mining industry and location within the Lower Silesian region.
  • A. 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.
  • B. Tczew
    Tczew is a historic town in northern Poland on the Vistula River, known for its important railway bridges and role as a regional transport hub.
  • C. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • D. Wadowice
    Wadowice is a historic town in southern Poland best known as the birthplace of Pope John Paul II.
  • E. Jaworzno
    Jaworzno is a city in southern Poland, located in the Silesian Voivodeship and known for its industrial heritage and role in the Upper Silesian urban area.
  • 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_69a49917dfc081909acdbdf5d684f1ef completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c581714881909bf4c2bad9645176 completed March 1, 2026, 11:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69b108b2902481908a6e9915e95a7641 completed March 11, 2026, 6:16 a.m.
NEDg Description generation batch_69b10b4b8c24819096175f6e4443c246 completed March 11, 2026, 6:27 a.m.
NED2 Entity disambiguation (via description) batch_69b10bc9d6648190bfb161bc7699926e completed March 11, 2026, 6:29 a.m.
Created at: March 1, 2026, 8 p.m.