Triple

T1862381
Position Surface form Disambiguated ID Type / Status
Subject Ems E34843 entity
Predicate tributary P415 FINISHED
Object Werse
The Werse is a river in North Rhine-Westphalia, Germany, known for flowing through the Münster region before joining the Ems.
E244607 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: Werse | Statement: [Ems, tributary, Werse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Werse
Context triple: [Ems, tributary, Werse]
  • A. Severn
    The Severn is the longest river in Great Britain, flowing through Wales and England before emptying into the Bristol Channel.
  • B. River Darent
    River Darent is a chalk stream river in Kent, England, flowing north through the Darent Valley to join the River Thames.
  • C. River Ouse
    The River Ouse is a major river in North Yorkshire, England, flowing through the historic city of York and forming part of the Humber river system before reaching the North Sea.
  • D. Thames
    The Thames is a major river in southern England that flows through London and has long been central to the country’s history, commerce, and culture.
  • E. River Cam
    The River Cam is a picturesque river in eastern England best known for flowing through the historic city and university of Cambridge, where it is famous for punting and scenic college views.
  • 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: Werse
Triple: [Ems, tributary, Werse]
Generated description
The Werse is a river in North Rhine-Westphalia, Germany, known for flowing through the Münster region before joining the Ems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Werse
Target entity description: The Werse is a river in North Rhine-Westphalia, Germany, known for flowing through the Münster region before joining the Ems.
  • A. Severn
    The Severn is the longest river in Great Britain, flowing through Wales and England before emptying into the Bristol Channel.
  • B. River Darent
    River Darent is a chalk stream river in Kent, England, flowing north through the Darent Valley to join the River Thames.
  • C. River Ouse
    The River Ouse is a major river in North Yorkshire, England, flowing through the historic city of York and forming part of the Humber river system before reaching the North Sea.
  • D. Thames
    The Thames is a major river in southern England that flows through London and has long been central to the country’s history, commerce, and culture.
  • E. River Cam
    The River Cam is a picturesque river in eastern England best known for flowing through the historic city and university of Cambridge, where it is famous for punting and scenic college views.
  • 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_69ae6522bb448190ae50a476da53fcda completed March 9, 2026, 6:13 a.m.
NEDg Description generation batch_69ae666bd32c81909ff15201757a6c76 completed March 9, 2026, 6:19 a.m.
NED2 Entity disambiguation (via description) batch_69ae66d8ba688190a2102c00fc6231c4 completed March 9, 2026, 6:21 a.m.
Created at: March 4, 2026, 7:34 p.m.