Triple

T979937
Position Surface form Disambiguated ID Type / Status
Subject Grand Est E21143 entity
Predicate containsDepartment P1467 FINISHED
Object Marne
Marne is a department in northeastern France known for its Champagne-producing vineyards and historic World War I battlefields.
 E46315 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: [Grand Est, containsDepartment, Marne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marne
Context triple: [Grand Est, containsDepartment, 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. Aisne
    Aisne is a department in northern France known for its historic towns, World War I battlefields, and rural landscapes.
  • C. Nièvre
    Nièvre is a rural department in central France’s Bourgogne-Franche-Comté region, known for its rolling countryside, the Loire River, and its capital city Nevers.
  • D. Somme River
    The Somme River is a waterway in northern France that became historically significant as the site of one of World War I’s largest and bloodiest battles.
  • E. Moselle
    Moselle is a department in northeastern France, bordering Germany and Luxembourg, known for its strategic location, industrial history, and mixed French-German cultural heritage.
  • 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: [Grand Est, containsDepartment, Marne]
Generated description
Marne is a department in northeastern France known for its Champagne-producing vineyards and historic World War I battlefields.
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 World War I battlefields.
  • A. Marne chosen
    The Marne is a major river in northeastern France that flows through the Île-de-France region before joining the Seine near Paris.
  • B. Aisne
    Aisne is a department in northern France known for its historic towns, World War I battlefields, and rural landscapes.
  • C. Nièvre
    Nièvre is a rural department in central France’s Bourgogne-Franche-Comté region, known for its rolling countryside, the Loire River, and its capital city Nevers.
  • D. Somme River
    The Somme River is a waterway in northern France that became historically significant as the site of one of World War I’s largest and bloodiest battles.
  • E. Moselle
    Moselle is a department in northeastern France, bordering Germany and Luxembourg, known for its strategic location, industrial history, and mixed French-German cultural heritage.
  • 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_69a493c2b62c8190b616351789ec47f8 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b47b58ec81908d95f151b9af3dae completed March 1, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69acc605c85881909adc6091bb9d9f8c completed March 8, 2026, 12:42 a.m.
NEDg Description generation batch_69acc71a3e808190aecbb57a64f39b6b completed March 8, 2026, 12:47 a.m.
NED2 Entity disambiguation (via description) batch_69acc88e4ec08190945b366524b83088 completed March 8, 2026, 12:53 a.m.
Created at: March 1, 2026, 7:40 p.m.