Triple

T2355565
Position Surface form Disambiguated ID Type / Status
Subject Emsland E47544 entity
Predicate borderingRegion P17964 FINISHED
Object Cloppenburg
Cloppenburg is a rural district in Lower Saxony, Germany, known for its agricultural economy and the open-air museum Museumsdorf Cloppenburg.
 E260262 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: Cloppenburg | Statement: [Emsland, borderingRegion, Cloppenburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cloppenburg
Context triple: [Emsland, borderingRegion, Cloppenburg]
  • A. Lüneburg
    Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
  • B. Pinneberg
    Pinneberg is a town in northern Germany that serves as the administrative center of the district of the same name near Hamburg.
  • C. Emsland
    Emsland is a rural region in western Germany known for its agriculture, peatlands, and location along the River Ems near the Dutch border.
  • D. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • E. Münsterland
    Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
  • 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: Cloppenburg
Triple: [Emsland, borderingRegion, Cloppenburg]
Generated description
Cloppenburg is a rural district in Lower Saxony, Germany, known for its agricultural economy and the open-air museum Museumsdorf Cloppenburg.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cloppenburg
Target entity description: Cloppenburg is a rural district in Lower Saxony, Germany, known for its agricultural economy and the open-air museum Museumsdorf Cloppenburg.
  • A. Lüneburg
    Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
  • B. Pinneberg
    Pinneberg is a town in northern Germany that serves as the administrative center of the district of the same name near Hamburg.
  • C. Emsland
    Emsland is a rural region in western Germany known for its agriculture, peatlands, and location along the River Ems near the Dutch border.
  • D. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • E. Münsterland
    Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
  • 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_69a88a1b678c8190bce986922ba60ce0 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc6fd4e488190b763a1c9b5d18f2c completed March 7, 2026, 6:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea888b5a881909b1f91562957388d completed March 9, 2026, 11:01 a.m.
NEDg Description generation batch_69aea9e4fd748190870fca46e6d2ea78 completed March 9, 2026, 11:07 a.m.
NED2 Entity disambiguation (via description) batch_69aeaa3f5afc8190af11862c52f35074 completed March 9, 2026, 11:08 a.m.
Created at: March 4, 2026, 7:54 p.m.