Triple

T12566849
Position Surface form Disambiguated ID Type / Status
Subject Province of Westphalia E295497 entity
Predicate containsSettlement P847 FINISHED
Object Vechta
Vechta is a town in northwestern Germany known for its university, agricultural economy, and location within the state of Lower Saxony.
 E217603 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: Vechta | Statement: [Province of Westphalia, containsSettlement, Vechta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vechta
Context triple: [Province of Westphalia, containsSettlement, Vechta]
  • A. Vechta
    Vechta is a town in Lower Saxony, Germany, known for its historical significance, university, and annual Stoppelmarkt fair.
  • B. Sappemeer
    Sappemeer is a town in the province of Groningen in the northeastern Netherlands, historically known for its peat colonies and waterways.
  • C. Zwalm
    Zwalm is a rural municipality in East Flanders, Belgium, known for its scenic hilly landscape, watermills, and network of walking and cycling routes.
  • D. Salland
    Salland is a historical and rural region in the Dutch province of Overijssel, known for its scenic landscapes, small towns, and agricultural character.
  • E. Oostzaan
    Oostzaan is a small municipality in the province of North Holland in the Netherlands, located just north of Amsterdam.
  • 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: Vechta
Triple: [Province of Westphalia, containsSettlement, Vechta]
Generated description
Vechta is a town in northwestern Germany known for its university, agricultural economy, and location within the state of Lower Saxony.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vechta
Target entity description: Vechta is a town in northwestern Germany known for its university, agricultural economy, and location within the state of Lower Saxony.
  • A. Vechta chosen
    Vechta is a town in Lower Saxony, Germany, known for its historical significance, university, and annual Stoppelmarkt fair.
  • B. Sappemeer
    Sappemeer is a town in the province of Groningen in the northeastern Netherlands, historically known for its peat colonies and waterways.
  • C. Zwalm
    Zwalm is a rural municipality in East Flanders, Belgium, known for its scenic hilly landscape, watermills, and network of walking and cycling routes.
  • D. Salland
    Salland is a historical and rural region in the Dutch province of Overijssel, known for its scenic landscapes, small towns, and agricultural character.
  • E. Oostzaan
    Oostzaan is a small municipality in the province of North Holland in the Netherlands, located just north of Amsterdam.
  • F. None of above.

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_69d6ad9cac2c81908e8a7bed82d1e21d completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d954a325948190994bcfc9d571a3a8 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6af43f2188190b0e78f22dc6ba3f8 completed May 3, 2026, 2:13 a.m.
NEDg Description generation batch_69f6b0653ef88190ad0e3a48675ecdcc completed May 3, 2026, 2:18 a.m.
NED2 Entity disambiguation (via description) batch_69f6b1ae645c8190a7c3e5f926c6a487 completed May 3, 2026, 2:23 a.m.
Created at: April 8, 2026, 11:49 p.m.