Triple

T2337498
Position Surface form Disambiguated ID Type / Status
Subject Hamburg Airport E44344 entity
Predicate locatedInDistrict P40 FINISHED
Object Groß Borstel
Groß Borstel is a residential district of Hamburg, Germany, situated near Hamburg Airport and characterized by a mix of urban housing and green spaces.
 E258719 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: Groß Borstel | Statement: [Hamburg Airport, locatedInDistrict, Groß Borstel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Groß Borstel
Context triple: [Hamburg Airport, locatedInDistrict, Groß Borstel]
  • A. Elmshorn
    Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
  • B. Aurich
    Aurich is a historic town in northwestern Germany that serves as one of the principal urban centers of the East Frisia region in Lower Saxony.
  • C. Pinneberg
    Pinneberg is a town in northern Germany that serves as the administrative center of the district of the same name near Hamburg.
  • D. Eckernförde
    Eckernförde is a coastal town in northern Germany known for its Baltic Sea beaches, historic harbor, and maritime tourism.
  • E. Lingen
    Lingen is a town in Lower Saxony, Germany, known for its location on the River Ems and its role as a regional economic and cultural center.
  • 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: Groß Borstel
Triple: [Hamburg Airport, locatedInDistrict, Groß Borstel]
Generated description
Groß Borstel is a residential district of Hamburg, Germany, situated near Hamburg Airport and characterized by a mix of urban housing and green spaces.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Groß Borstel
Target entity description: Groß Borstel is a residential district of Hamburg, Germany, situated near Hamburg Airport and characterized by a mix of urban housing and green spaces.
  • A. Elmshorn
    Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
  • B. Aurich
    Aurich is a historic town in northwestern Germany that serves as one of the principal urban centers of the East Frisia region in Lower Saxony.
  • C. Pinneberg
    Pinneberg is a town in northern Germany that serves as the administrative center of the district of the same name near Hamburg.
  • D. Eckernförde
    Eckernförde is a coastal town in northern Germany known for its Baltic Sea beaches, historic harbor, and maritime tourism.
  • E. Lingen
    Lingen is a town in Lower Saxony, Germany, known for its location on the River Ems and its role as a regional economic and cultural center.
  • 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_69a889132b488190bbb43ad4780ddd92 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abc68ac4348190ab6ec46ec7879643 completed March 7, 2026, 6:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae961adfdc8190bf79d479d8207599 completed March 9, 2026, 9:42 a.m.
NEDg Description generation batch_69ae97406c588190855ecb6c9ed00bc9 completed March 9, 2026, 9:47 a.m.
NED2 Entity disambiguation (via description) batch_69ae97c82e8c8190a613444ea4ffb5a0 completed March 9, 2026, 9:50 a.m.
Created at: March 4, 2026, 7:51 p.m.