Triple

T19183782
Position Surface form Disambiguated ID Type / Status
Subject Sachsenwald E469644 entity
Predicate near P350 FINISHED
Object Wentorf bei Hamburg
Wentorf bei Hamburg is a small municipality in northern Germany’s Schleswig-Holstein state, located just southeast of Hamburg and known for its proximity to the historic Sachsenwald forest.
 E1362858 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: Wentorf bei Hamburg | Statement: [Sachsenwald, near, Wentorf bei Hamburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wentorf bei Hamburg
Context triple: [Sachsenwald, near, Wentorf bei Hamburg]
  • A. Wallenhorst
    Wallenhorst is a municipality in Lower Saxony, Germany, located near the city of Osnabrück.
  • B. Oerlinghausen
    Oerlinghausen is a small town in the German state of North Rhine-Westphalia, known for its scenic Teutoburg Forest surroundings and historical roots.
  • C. Northeim
    Northeim is a town in Lower Saxony, Germany, known for its medieval old town and location in the Leine River valley.
  • D. Hasselwerder
    Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
  • E. Warendorf
    Warendorf is a historic town in western Germany’s North Rhine-Westphalia, known for its well-preserved medieval old town and strong equestrian traditions.
  • 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: Wentorf bei Hamburg
Triple: [Sachsenwald, near, Wentorf bei Hamburg]
Generated description
Wentorf bei Hamburg is a small municipality in northern Germany’s Schleswig-Holstein state, located just southeast of Hamburg and known for its proximity to the historic Sachsenwald forest.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wentorf bei Hamburg
Target entity description: Wentorf bei Hamburg is a small municipality in northern Germany’s Schleswig-Holstein state, located just southeast of Hamburg and known for its proximity to the historic Sachsenwald forest.
  • A. Wallenhorst
    Wallenhorst is a municipality in Lower Saxony, Germany, located near the city of Osnabrück.
  • B. Oerlinghausen
    Oerlinghausen is a small town in the German state of North Rhine-Westphalia, known for its scenic Teutoburg Forest surroundings and historical roots.
  • C. Northeim
    Northeim is a town in Lower Saxony, Germany, known for its medieval old town and location in the Leine River valley.
  • D. Hasselwerder
    Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
  • E. Warendorf
    Warendorf is a historic town in western Germany’s North Rhine-Westphalia, known for its well-preserved medieval old town and strong equestrian traditions.
  • 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_69d8dd0ad9088190a173b32657ae2e7a completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5f61f1d9c8190b67555383d821958 completed April 20, 2026, 9:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a06f8b818b481908c27575369db40b2 completed May 15, 2026, 10:43 a.m.
NEDg Description generation batch_6a06f987d3e88190ade6412afd3e1426 completed May 15, 2026, 10:46 a.m.
NED2 Entity disambiguation (via description) batch_6a06fa6c6e5481909beb3a33517264f3 completed May 15, 2026, 10:50 a.m.
Created at: April 10, 2026, 12:07 p.m.