Triple

T2049912
Position Surface form Disambiguated ID Type / Status
Subject Schleswig-Holstein E45540 entity
Predicate hasCity P316 FINISHED
Object Pinneberg
Pinneberg is a town in northern Germany that serves as the administrative center of the district of the same name near Hamburg.
 E250427 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: Pinneberg | Statement: [Schleswig-Holstein, hasCity, Pinneberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pinneberg
Context triple: [Schleswig-Holstein, hasCity, Pinneberg]
  • A. Lüneburg
    Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
  • B. Papenburg
    Papenburg is a German town in Lower Saxony best known for its historic canals and its large Meyer Werft shipyard, one of the world’s leading builders of cruise ships.
  • C. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • D. Husum
    Husum is a small coastal town in northern Germany known for its North Sea harbor, maritime heritage, and role as a local cultural and commercial center.
  • E. Wolfenbüttel
    Wolfenbüttel is a historic town in Lower Saxony, Germany, known for its Renaissance castle and rich cultural heritage.
  • 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: Pinneberg
Triple: [Schleswig-Holstein, hasCity, Pinneberg]
Generated description
Pinneberg is a town in northern Germany that serves as the administrative center of the district of the same name near Hamburg.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pinneberg
Target entity description: Pinneberg is a town in northern Germany that serves as the administrative center of the district of the same name near Hamburg.
  • A. Lüneburg
    Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
  • B. Papenburg
    Papenburg is a German town in Lower Saxony best known for its historic canals and its large Meyer Werft shipyard, one of the world’s leading builders of cruise ships.
  • C. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • D. Husum
    Husum is a small coastal town in northern Germany known for its North Sea harbor, maritime heritage, and role as a local cultural and commercial center.
  • E. Wolfenbüttel
    Wolfenbüttel is a historic town in Lower Saxony, Germany, known for its Renaissance castle and rich cultural heritage.
  • 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_69a8891948208190ab7898da21824c77 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb98e10d48190bb96cd1f8ea3c08b completed March 7, 2026, 5:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae71a7e3408190955aa7f2534316dc completed March 9, 2026, 7:07 a.m.
NEDg Description generation batch_69ae74190ac481908d1a54fb744e7df3 completed March 9, 2026, 7:17 a.m.
NED2 Entity disambiguation (via description) batch_69ae7472fe948190b8df210de1afb159 completed March 9, 2026, 7:19 a.m.
Created at: March 4, 2026, 7:39 p.m.