Triple

T8346499
Position Surface form Disambiguated ID Type / Status
Subject Koblenz E196047 entity
Predicate hasTwinTown P919 FINISHED
Object Opole E28226 NE FINISHED

How this triple was built (2 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: [Koblenz, hasTwinTown, Opole]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Opole
Context triple: [Koblenz, hasTwinTown, Opole]
  • A. Opole chosen
    Opole is a historic city in southwestern Poland, known as one of the country’s oldest urban centers and a regional cultural hub.
  • B. Opole Silesia
    Opole Silesia is a historical and cultural region in southwestern Poland, known for its mixed Polish-German heritage and centered around the city of Opole.
  • C. Opole Lubelskie
    Opole Lubelskie is a small town in eastern Poland known for its agricultural surroundings and location within the Lublin region.
  • D. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca82edd63c8190b876b8465464c5fa completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb8013d81c81908da48466cffb3939 completed March 31, 2026, 8:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69d316e50b548190b5f90a9753ad7cb0 completed April 6, 2026, 2:13 a.m.
Created at: March 30, 2026, 5:58 p.m.