Triple

T1321462
Position Surface form Disambiguated ID Type / Status
Subject Opole E28226 entity
Predicate historicalName P65 FINISHED
Object Oppeln
Oppeln is the historical German name for the city of Opole, a major cultural and administrative center in southwestern Poland’s Silesia region.
 E234318 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: Oppeln | Statement: [Opole, historicalName, Oppeln]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Oppeln
Context triple: [Opole, historicalName, Oppeln]
  • A. Görlitz
    Görlitz is a historic city in eastern Germany on the Lusatian Neisse River, known for its well-preserved old town and role as a popular film location.
  • B. Frankfurt (Oder)
    Frankfurt (Oder) is a German city on the Oder River at the Polish border, known as a historic university and trade center in the state of Brandenburg.
  • C. Chemnitz
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • D. Opava
    Opava is a historic city in the Czech Republic’s Silesian region, known as a former political and cultural center of Silesia.
  • E. Lankwitz
    Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
  • 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: Oppeln
Triple: [Opole, historicalName, Oppeln]
Generated description
Oppeln is the historical German name for the city of Opole, a major cultural and administrative center in southwestern Poland’s Silesia region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Oppeln
Target entity description: Oppeln is the historical German name for the city of Opole, a major cultural and administrative center in southwestern Poland’s Silesia region.
  • A. Görlitz
    Görlitz is a historic city in eastern Germany on the Lusatian Neisse River, known for its well-preserved old town and role as a popular film location.
  • B. Frankfurt (Oder)
    Frankfurt (Oder) is a German city on the Oder River at the Polish border, known as a historic university and trade center in the state of Brandenburg.
  • C. Chemnitz
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • D. Opava
    Opava is a historic city in the Czech Republic’s Silesian region, known as a former political and cultural center of Silesia.
  • E. Lankwitz
    Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
  • 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_69a498540a2481909e807a762280d3ba completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c19932888190a3d45871e84f112e completed March 1, 2026, 10:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae3037944c8190b2ced5f5ca539260 completed March 9, 2026, 2:28 a.m.
NEDg Description generation batch_69ae3149922c819085fb2af51d53304c completed March 9, 2026, 2:32 a.m.
NED2 Entity disambiguation (via description) batch_69ae31a67eb08190a3ef64e83301fabc completed March 9, 2026, 2:34 a.m.
Created at: March 1, 2026, 7:55 p.m.