Triple

T170662
Position Surface form Disambiguated ID Type / Status
Subject Székesfehérvár E3113 entity
Predicate hasTwinTown P919 FINISHED
Object Opole
Opole is a historic city in southwestern Poland, known as one of the country’s oldest urban centers and a regional cultural hub.
 E28226 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: Opole | Statement: [Székesfehérvár, hasTwinTown, Opole]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Opole
Context triple: [Székesfehérvár, hasTwinTown, Opole]
  • A. Poznań
    Poznań is a historic and economically significant city in western Poland, known for its medieval Old Town, role as an early center of Polish statehood, and status as a major academic and industrial hub.
  • B. 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.
  • C. Łódź
    Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
  • D. Tarnów
    Tarnów is a historic city in southern Poland known for its well-preserved Old Town, Renaissance architecture, and cultural heritage.
  • E. Glogów
    Glogów is a historic town in western Poland on the Oder River, known for its medieval origins and reconstructed Old Town.
  • 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: Opole
Triple: [Székesfehérvár, hasTwinTown, Opole]
Generated description
Opole is a historic city in southwestern Poland, known as one of the country’s oldest urban centers and a regional cultural hub.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Opole
Target entity description: Opole is a historic city in southwestern Poland, known as one of the country’s oldest urban centers and a regional cultural hub.
  • A. Poznań
    Poznań is a historic and economically significant city in western Poland, known for its medieval Old Town, role as an early center of Polish statehood, and status as a major academic and industrial hub.
  • B. 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.
  • C. Łódź
    Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
  • D. Tarnów
    Tarnów is a historic city in southern Poland known for its well-preserved Old Town, Renaissance architecture, and cultural heritage.
  • E. Glogów
    Glogów is a historic town in western Poland on the Oder River, known for its medieval origins and reconstructed Old Town.
  • 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_69a2524ce1e48190ab066bf72859f474 completed Feb. 28, 2026, 2:26 a.m.
NER Named-entity recognition batch_69a258b82bdc81908ebd50fb05d511df completed Feb. 28, 2026, 2:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69a34d90d3948190a59a53e54b477681 completed Feb. 28, 2026, 8:18 p.m.
NEDg Description generation batch_69a34e222c388190861811c2f6aff0a6 completed Feb. 28, 2026, 8:20 p.m.
NED2 Entity disambiguation (via description) batch_69a34e9e185c8190b03d8906082928fe completed Feb. 28, 2026, 8:22 p.m.
Created at: Feb. 28, 2026, 2:34 a.m.