Triple

T12083020
Position Surface form Disambiguated ID Type / Status
Subject Armançon E287728 entity
Predicate flowsThrough P225 FINISHED
Object Buffon
Buffon is a commune in the Côte-d'Or department of eastern France, known for its historical industrial heritage and rural setting along the Armançon River.
 E968119 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: Buffon | Statement: [Armançon, flowsThrough, Buffon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Buffon
Context triple: [Armançon, flowsThrough, Buffon]
  • A. Buffon
    Buffon was an 18th-century French naturalist whose pioneering work in natural history and ideas about species and the age of the Earth significantly shaped early evolutionary thought.
  • B. Edmé
    Edmé is a French given name historically borne by figures such as the 18th-century sculptor and draftsman Edmé Bouchardon.
  • C. Pierre Poisson
    Pierre Poisson was a French architect known for his work on the historic Palais des Papes in Avignon.
  • D. Bézu Fache
    Bézu Fache is the stern and devout captain of the French Judicial Police who leads the investigation at the Louvre in Dan Brown’s novel *The Da Vinci Code*.
  • E. Pierre Simon
    Pierre Simon is a French politician who has served as a member of the Senate of France, representing the department of Côtes-d'Armor.
  • 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: Buffon
Triple: [Armançon, flowsThrough, Buffon]
Generated description
Buffon is a commune in the Côte-d'Or department of eastern France, known for its historical industrial heritage and rural setting along the Armançon River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Buffon
Target entity description: Buffon is a commune in the Côte-d'Or department of eastern France, known for its historical industrial heritage and rural setting along the Armançon River.
  • A. Buffon
    Buffon was an 18th-century French naturalist whose pioneering work in natural history and ideas about species and the age of the Earth significantly shaped early evolutionary thought.
  • B. Edmé
    Edmé is a French given name historically borne by figures such as the 18th-century sculptor and draftsman Edmé Bouchardon.
  • C. Pierre Poisson
    Pierre Poisson was a French architect known for his work on the historic Palais des Papes in Avignon.
  • D. Bézu Fache
    Bézu Fache is the stern and devout captain of the French Judicial Police who leads the investigation at the Louvre in Dan Brown’s novel *The Da Vinci Code*.
  • E. Pierre Simon
    Pierre Simon is a French politician who has served as a member of the Senate of France, representing the department of Côtes-d'Armor.
  • 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_69d6ab4964708190850585628b287b0c completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915124e4c8190b0264c2a09e3c2f3 completed April 10, 2026, 3:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f66509208190b7206e78df41c2fe completed May 2, 2026, 1:04 p.m.
NEDg Description generation batch_69f6022ecf38819080f0eb6a3a815c5b completed May 2, 2026, 1:54 p.m.
NED2 Entity disambiguation (via description) batch_69f606560934819092ba4d4fa162b799 completed May 2, 2026, 2:12 p.m.
Created at: April 8, 2026, 9:48 p.m.