Triple

T14017834
Position Surface form Disambiguated ID Type / Status
Subject Fleesensee E337244 entity
Predicate hasNearbySettlement P4647 FINISHED
Object Malchow
Malchow is a small town in the Mecklenburg Lake District of northeastern Germany, known for its lakeside setting and historic island old town.
E1076289 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: Malchow | Statement: [Fleesensee, hasNearbySettlement, Malchow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Malchow
Context triple: [Fleesensee, hasNearbySettlement, Malchow]
  • A. Malchow
    Malchow is a small locality within the Berlin borough of Lichtenberg, known for its more rural character and green surroundings compared to the inner city.
  • B. Melchow
    Melchow is a small municipality in the Barnim district of the federal state of Brandenburg in northeastern Germany.
  • C. Maienwerder
    Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
  • D. Borghorst
    Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
  • E. Havixbeck
    Havixbeck is a municipality in North Rhine-Westphalia, Germany, known for its rural character and proximity to the city of Münster.
  • 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: Malchow
Triple: [Fleesensee, hasNearbySettlement, Malchow]
Generated description
Malchow is a small town in the Mecklenburg Lake District of northeastern Germany, known for its lakeside setting and historic island old town.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Malchow
Target entity description: Malchow is a small town in the Mecklenburg Lake District of northeastern Germany, known for its lakeside setting and historic island old town.
  • A. Malchow
    Malchow is a small locality within the Berlin borough of Lichtenberg, known for its more rural character and green surroundings compared to the inner city.
  • B. Melchow
    Melchow is a small municipality in the Barnim district of the federal state of Brandenburg in northeastern Germany.
  • C. Maienwerder
    Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
  • D. Borghorst
    Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
  • E. Havixbeck
    Havixbeck is a municipality in North Rhine-Westphalia, Germany, known for its rural character and proximity to the city of Münster.
  • 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_69d81c6543a48190bd5ba93d7419e797 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2f3b5b088190a58715779d2c46a6 completed April 14, 2026, 12:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbc32d77108190b038e8a750738439 completed May 6, 2026, 10:39 p.m.
NEDg Description generation batch_69fc43e28e288190827925f45b942959 completed May 7, 2026, 7:48 a.m.
NED2 Entity disambiguation (via description) batch_69fc444630408190b368611373265973 completed May 7, 2026, 7:50 a.m.
Created at: April 9, 2026, 10:19 p.m.