Triple

T18438995
Position Surface form Disambiguated ID Type / Status
Subject Marzahn-Hellersdorf E450473 entity
Predicate hasSubdistrict P747 FINISHED
Object Kaulsdorf
Kaulsdorf is a residential locality in eastern Berlin, Germany, known for its mix of historic village center and post-war housing estates.
 E1361888 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: Kaulsdorf | Statement: [Marzahn-Hellersdorf, hasSubdistrict, Kaulsdorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kaulsdorf
Context triple: [Marzahn-Hellersdorf, hasSubdistrict, Kaulsdorf]
  • A. Kaldenkirchen
    Kaldenkirchen is a town in western Germany near the Dutch border, known as a key cross-border transport point with direct access to major motorway routes.
  • B. Sulzheim
    Sulzheim is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
  • C. Dierdorf
    Dierdorf is a surname most prominently associated with former American football player and sportscaster Dan Dierdorf.
  • D. Marlenheim
    Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
  • E. Kesselsdorf
    Kesselsdorf is a village in Saxony, Germany, historically notable as the site of a major battle during the Second Silesian War.
  • 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: Kaulsdorf
Triple: [Marzahn-Hellersdorf, hasSubdistrict, Kaulsdorf]
Generated description
Kaulsdorf is a residential locality in eastern Berlin, Germany, known for its mix of historic village center and post-war housing estates.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kaulsdorf
Target entity description: Kaulsdorf is a residential locality in eastern Berlin, Germany, known for its mix of historic village center and post-war housing estates.
  • A. Kaldenkirchen
    Kaldenkirchen is a town in western Germany near the Dutch border, known as a key cross-border transport point with direct access to major motorway routes.
  • B. Sulzheim
    Sulzheim is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
  • C. Dierdorf
    Dierdorf is a surname most prominently associated with former American football player and sportscaster Dan Dierdorf.
  • D. Marlenheim
    Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
  • E. Kesselsdorf
    Kesselsdorf is a village in Saxony, Germany, historically notable as the site of a major battle during the Second Silesian War.
  • 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_69d8d381d6388190a9e94e9c658174e4 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e51c0ed6708190ae90efd8455ec352 completed April 19, 2026, 6:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a06f2372be48190bff30e8280a46efe completed May 15, 2026, 10:15 a.m.
NEDg Description generation batch_6a06f328c18881909948ed66d50b9eae completed May 15, 2026, 10:19 a.m.
NED2 Entity disambiguation (via description) batch_6a06f4f990808190b3b8e21f85d20219 completed May 15, 2026, 10:27 a.m.
Created at: April 10, 2026, 11:30 a.m.