Triple

T3361530
Position Surface form Disambiguated ID Type / Status
Subject Southern Poland E70731 entity
Predicate hasMajorCity P316 FINISHED
Object Zabrze
Zabrze is an industrial city in the Silesian region of southern Poland, historically known for coal mining and heavy industry.
 E526141 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: Zabrze | Statement: [Southern Poland, hasMajorCity, Zabrze]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zabrze
Context triple: [Southern Poland, hasMajorCity, Zabrze]
  • A. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • B. Zgierz
    Zgierz is a city in central Poland, historically part of the industrial Łódź region and notable for its textile industry and role in regional trade.
  • C. Tychy
    Tychy is a city in the Silesian region of southern Poland, known for its brewing industry and role as a planned industrial center.
  • D. Bielsko-Biała
    Bielsko-Biała is a city in southern Poland at the foot of the Beskid Mountains, known as a regional industrial and cultural center formed from the historic towns of Bielsko and Biała.
  • E. Zduńska Wola
    Zduńska Wola is a town in central Poland known historically as a textile and industrial 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: Zabrze
Triple: [Southern Poland, hasMajorCity, Zabrze]
Generated description
Zabrze is an industrial city in the Silesian region of southern Poland, historically known for coal mining and heavy industry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Zabrze
Target entity description: Zabrze is an industrial city in the Silesian region of southern Poland, historically known for coal mining and heavy industry.
  • A. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • B. Zgierz
    Zgierz is a city in central Poland, historically part of the industrial Łódź region and notable for its textile industry and role in regional trade.
  • C. Tychy
    Tychy is a city in the Silesian region of southern Poland, known for its brewing industry and role as a planned industrial center.
  • D. Bielsko-Biała
    Bielsko-Biała is a city in southern Poland at the foot of the Beskid Mountains, known as a regional industrial and cultural center formed from the historic towns of Bielsko and Biała.
  • E. Zduńska Wola
    Zduńska Wola is a town in central Poland known historically as a textile and industrial 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_69ad85a660c48190998489309a3b4869 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb26906948190851a7b7d543a4d64 completed March 8, 2026, 5:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfaf20b6408190ae0ab543361a0eb2 completed March 22, 2026, 8:58 a.m.
NEDg Description generation batch_69bfaf8ff3748190932e03bc3966d59e completed March 22, 2026, 9 a.m.
NED2 Entity disambiguation (via description) batch_69bfb037ea9c819094e14419ec060018 completed March 22, 2026, 9:02 a.m.
Created at: March 8, 2026, 3:13 p.m.