Triple

T2049908
Position Surface form Disambiguated ID Type / Status
Subject Schleswig-Holstein E45540 entity
Predicate hasCity P316 FINISHED
Object 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.
 E246172 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: Husum | Statement: [Schleswig-Holstein, hasCity, Husum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Husum
Context triple: [Schleswig-Holstein, hasCity, Husum]
  • A. 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.
  • B. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • C. Bremerhaven
    Bremerhaven is a major German port city on the North Sea, known for its maritime industry, shipbuilding, and role as a key hub for trade and logistics.
  • D. Elmshorn
    Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
  • E. Eckernförde
    Eckernförde is a coastal town in northern Germany known for its Baltic Sea beaches, historic harbor, and maritime tourism.
  • 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: Husum
Triple: [Schleswig-Holstein, hasCity, Husum]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Husum
Target entity description: 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.
  • A. 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.
  • B. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • C. Bremerhaven
    Bremerhaven is a major German port city on the North Sea, known for its maritime industry, shipbuilding, and role as a key hub for trade and logistics.
  • D. Elmshorn
    Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
  • E. Eckernförde
    Eckernförde is a coastal town in northern Germany known for its Baltic Sea beaches, historic harbor, and maritime tourism.
  • 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_69ae652a87748190b9e18356ffb13bed completed March 9, 2026, 6:14 a.m.
NEDg Description generation batch_69ae669aa29c81909770cd69d27c274c completed March 9, 2026, 6:20 a.m.
NED2 Entity disambiguation (via description) batch_69ae66fb7f1c8190b2bc306f06c423f1 completed March 9, 2026, 6:21 a.m.
Created at: March 4, 2026, 7:39 p.m.