Triple

T11312357
Position Surface form Disambiguated ID Type / Status
Subject Notre-Dame-en-Vaux E267869 entity
Predicate locatedInDepartment P40 FINISHED
Object Marne
Marne is a department in northeastern France known for its Champagne-producing vineyards and historic towns such as Reims and Châlons-en-Champagne.
 E992253 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: Marne | Statement: [Notre-Dame-en-Vaux, locatedInDepartment, Marne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marne
Context triple: [Notre-Dame-en-Vaux, locatedInDepartment, Marne]
  • A. Marne
    The Marne is a major river in northeastern France that flows through the Île-de-France region before joining the Seine near Paris.
  • B. Marne
    Marne is a small city located in Cass County in the southwestern part of the U.S. state of Iowa.
  • C. Aisne
    Aisne is a department in northern France known for its historic towns, World War I battlefields, and rural landscapes.
  • D. Aisne
    Aisne is a river in northeastern France that flows through the Champagne and Picardy regions before joining the Oise River.
  • E. Vallée de la Marne
    Vallée de la Marne is a key subregion of France’s Champagne wine area, known for its vineyards along the Marne River and its significant production of Pinot Meunier–based sparkling wines.
  • 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: Marne
Triple: [Notre-Dame-en-Vaux, locatedInDepartment, Marne]
Generated description
Marne is a department in northeastern France known for its Champagne-producing vineyards and historic towns such as Reims and Châlons-en-Champagne.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marne
Target entity description: Marne is a department in northeastern France known for its Champagne-producing vineyards and historic towns such as Reims and Châlons-en-Champagne.
  • A. Marne
    The Marne is a major river in northeastern France that flows through the Île-de-France region before joining the Seine near Paris.
  • B. Marne
    Marne is a small city located in Cass County in the southwestern part of the U.S. state of Iowa.
  • C. Aisne
    Aisne is a department in northern France known for its historic towns, World War I battlefields, and rural landscapes.
  • D. Aisne
    Aisne is a river in northeastern France that flows through the Champagne and Picardy regions before joining the Oise River.
  • E. Vallée de la Marne
    Vallée de la Marne is a key subregion of France’s Champagne wine area, known for its vineyards along the Marne River and its significant production of Pinot Meunier–based sparkling wines.
  • 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_69d6aaca5c24819083db46a30d86cb34 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9c1b7dc81908d8cc768c47390d3 completed April 9, 2026, 6:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65e9136bc8190b35685376da7007e completed May 2, 2026, 8:29 p.m.
NEDg Description generation batch_69f660bc541c8190a4d1d7a4cc959ecf completed May 2, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_69f6617997188190bfce14c54619af7f completed May 2, 2026, 8:41 p.m.
Created at: April 8, 2026, 9:32 p.m.