Triple

T1862378
Position Surface form Disambiguated ID Type / Status
Subject Ems E34843 entity
Predicate majorCityOnRiver P316 FINISHED
Object Lingen
Lingen is a town in Lower Saxony, Germany, known for its location on the River Ems and its role as a regional economic and cultural center.
 E217689 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: Lingen | Statement: [Ems, majorCityOnRiver, Lingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lingen
Context triple: [Ems, majorCityOnRiver, Lingen]
  • 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. Lauenburg
    Lauenburg is a historic town in northern Germany situated on the banks of the Elbe River.
  • C. Schwarmstedt
    Schwarmstedt is a municipality in Lower Saxony, Germany, situated in the Heidekreis district along the River Aller.
  • D. Elmshorn
    Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
  • E. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • 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: Lingen
Triple: [Ems, majorCityOnRiver, Lingen]
Generated description
Lingen is a town in Lower Saxony, Germany, known for its location on the River Ems and its role as a regional economic and cultural center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lingen
Target entity description: Lingen is a town in Lower Saxony, Germany, known for its location on the River Ems and its role as a regional economic and cultural 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. Lauenburg
    Lauenburg is a historic town in northern Germany situated on the banks of the Elbe River.
  • C. Schwarmstedt
    Schwarmstedt is a municipality in Lower Saxony, Germany, situated in the Heidekreis district along the River Aller.
  • D. Elmshorn
    Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
  • E. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • 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_69a88600b2f88190bc09303e68ab517e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abb09e714881909cef0f7e77b5b3b9 completed March 7, 2026, 4:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3c82d50819094e8ccdba0faf819 completed March 8, 2026, 10:10 p.m.
NEDg Description generation batch_69adf6023dc081908399c9ad402c7e82 completed March 8, 2026, 10:19 p.m.
NED2 Entity disambiguation (via description) batch_69adf65600448190ae90a954f88115ff completed March 8, 2026, 10:21 p.m.
Created at: March 4, 2026, 7:34 p.m.