Triple

T228683
Position Surface form Disambiguated ID Type / Status
Subject Lower Saxony E4364 entity
Predicate containsCity P294 FINISHED
Object Wilhelmshaven
Wilhelmshaven is a coastal city in northwestern Germany known for its major naval base and port on the North Sea.
 E41561 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: Wilhelmshaven | Statement: [Lower Saxony, containsCity, Wilhelmshaven]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wilhelmshaven
Context triple: [Lower Saxony, containsCity, Wilhelmshaven]
  • A. Dover
    Dover is a small town in eastern Dutchess County, New York, known for its rural character and location near the Connecticut border.
  • B. Dover
    Dover is a coastal town in southeast England best known for its white chalk cliffs and its strategic port facing the narrowest part of the English Channel.
  • C. Harwich
    Harwich is a coastal town on southeastern Cape Cod in Massachusetts known for its beaches, harbors, and summer tourism.
  • D. Portsmouth
    Portsmouth is a historic seaport city in southeastern New Hampshire known for its colonial-era architecture, maritime heritage, and vibrant cultural scene.
  • E. Portsmouth
    Portsmouth is a historic naval port city on England’s south coast, heavily targeted and damaged during the World War II Blitz.
  • 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: Wilhelmshaven
Triple: [Lower Saxony, containsCity, Wilhelmshaven]
Generated description
Wilhelmshaven is a coastal city in northwestern Germany known for its major naval base and port on the North Sea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wilhelmshaven
Target entity description: Wilhelmshaven is a coastal city in northwestern Germany known for its major naval base and port on the North Sea.
  • A. Dover
    Dover is a small town in eastern Dutchess County, New York, known for its rural character and location near the Connecticut border.
  • B. Dover
    Dover is a coastal town in southeast England best known for its white chalk cliffs and its strategic port facing the narrowest part of the English Channel.
  • C. Harwich
    Harwich is a coastal town on southeastern Cape Cod in Massachusetts known for its beaches, harbors, and summer tourism.
  • D. Portsmouth
    Portsmouth is a historic seaport city in southeastern New Hampshire known for its colonial-era architecture, maritime heritage, and vibrant cultural scene.
  • E. Portsmouth
    Portsmouth is a historic naval port city on England’s south coast, heavily targeted and damaged during the World War II Blitz.
  • 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_69a257363ffc81909757bde7ab3404da completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25c9140c48190b90647400854b37e completed Feb. 28, 2026, 3:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3cafa597881909ab227e02520b15c completed March 1, 2026, 5:13 a.m.
NEDg Description generation batch_69a3cbe58a4c8190876603a5ca545784 completed March 1, 2026, 5:17 a.m.
NED2 Entity disambiguation (via description) batch_69a3cc37976081909f13caa0d56fdd29 completed March 1, 2026, 5:18 a.m.
Created at: Feb. 28, 2026, 2:53 a.m.