Triple

T9189548
Position Surface form Disambiguated ID Type / Status
Subject Biała Piska E220545 entity
Predicate hasGermanName P1435 FINISHED
Object Gehlenburg
Gehlenburg is the former German name of the town now known as Biała Piska in northeastern Poland, reflecting its historical ties to East Prussia.
 E788131 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: Gehlenburg | Statement: [Biała Piska, hasGermanName, Gehlenburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gehlenburg
Context triple: [Biała Piska, hasGermanName, Gehlenburg]
  • A. Günsberg
    Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
  • B. Gehren
    Gehren is a small town in the German state of Thuringia, historically notable as the birthplace of Maria Barbara Bach, first wife of composer Johann Sebastian Bach.
  • C. Hammelburg
    Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
  • D. Ahrensburg
    Ahrensburg is a town in northern Germany’s Schleswig-Holstein state, known for its historic castle and proximity to Hamburg.
  • E. Gunzenhausen
    Gunzenhausen is a historic town in Bavaria, Germany, known for its location on the Altmühl River and as a gateway to the Franconian Lake District.
  • 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: Gehlenburg
Triple: [Biała Piska, hasGermanName, Gehlenburg]
Generated description
Gehlenburg is the former German name of the town now known as Biała Piska in northeastern Poland, reflecting its historical ties to East Prussia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gehlenburg
Target entity description: Gehlenburg is the former German name of the town now known as Biała Piska in northeastern Poland, reflecting its historical ties to East Prussia.
  • A. Günsberg
    Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
  • B. Gehren
    Gehren is a small town in the German state of Thuringia, historically notable as the birthplace of Maria Barbara Bach, first wife of composer Johann Sebastian Bach.
  • C. Hammelburg
    Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
  • D. Ahrensburg
    Ahrensburg is a town in northern Germany’s Schleswig-Holstein state, known for its historic castle and proximity to Hamburg.
  • E. Gunzenhausen
    Gunzenhausen is a historic town in Bavaria, Germany, known for its location on the Altmühl River and as a gateway to the Franconian Lake District.
  • 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_69ca83e6d77c81909862b7afef56b1bf completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccd5bd8c5c81909d0cdbcd7410fcee completed April 1, 2026, 8:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69d09b8b37488190b72f2b4c55fd9a8c completed April 4, 2026, 5:03 a.m.
NEDg Description generation batch_69d09cf11e488190b61f4a61002454e6 completed April 4, 2026, 5:09 a.m.
NED2 Entity disambiguation (via description) batch_69d09e2069048190ac22b738fa324771 completed April 4, 2026, 5:14 a.m.
Created at: March 30, 2026, 7:24 p.m.