Triple

T3409229
Position Surface form Disambiguated ID Type / Status
Subject Rhine–Herne Canal E71849 entity
Predicate endPoint P390 FINISHED
Object Herne
Herne is a city in the Ruhr area of North Rhine-Westphalia, Germany, known for its industrial heritage and dense urban character.
 E355366 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: Herne | Statement: [Rhine–Herne Canal, endPoint, Herne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Herne
Context triple: [Rhine–Herne Canal, endPoint, Herne]
  • A. Herne Hill
    Herne Hill is a residential district in South London known for its Victorian architecture, local markets, and proximity to Brockwell Park.
  • B. Reydon
    Reydon is a village and civil parish in the English county of Suffolk, located near the coastal town of Southwold.
  • C. Blackheath
    Blackheath is a historic village and popular tourist stop in the Blue Mountains of New South Wales, Australia, known for its dramatic cliffs, lookouts, and bushwalking trails.
  • D. Larkfield
    Larkfield is a village and civil parish in Kent, England, situated within the Tonbridge and Malling district.
  • E. Shudehill
    Shudehill is a central district in Manchester, England, known for its major bus and tram interchange and proximity to the city’s main shopping and commercial areas.
  • 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: Herne
Triple: [Rhine–Herne Canal, endPoint, Herne]
Generated description
Herne is a city in the Ruhr area of North Rhine-Westphalia, Germany, known for its industrial heritage and dense urban character.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Herne
Target entity description: Herne is a city in the Ruhr area of North Rhine-Westphalia, Germany, known for its industrial heritage and dense urban character.
  • A. Herne Hill
    Herne Hill is a residential district in South London known for its Victorian architecture, local markets, and proximity to Brockwell Park.
  • B. Reydon
    Reydon is a village and civil parish in the English county of Suffolk, located near the coastal town of Southwold.
  • C. Blackheath
    Blackheath is a historic village and popular tourist stop in the Blue Mountains of New South Wales, Australia, known for its dramatic cliffs, lookouts, and bushwalking trails.
  • D. Larkfield
    Larkfield is a village and civil parish in Kent, England, situated within the Tonbridge and Malling district.
  • E. Shudehill
    Shudehill is a central district in Manchester, England, known for its major bus and tram interchange and proximity to the city’s main shopping and commercial areas.
  • 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_69ad85ac312481909e7027ced1456a9f completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb90754788190ab85e2bec020f99e completed March 8, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69b34bdd99248190823875cae2531609 completed March 12, 2026, 11:27 p.m.
NEDg Description generation batch_69b34e486c3c81908e73c5b75baf119c completed March 12, 2026, 11:37 p.m.
NED2 Entity disambiguation (via description) batch_69b34fc420b08190baee678721b1b32c completed March 12, 2026, 11:44 p.m.
Created at: March 8, 2026, 3:15 p.m.