Triple

T14472009
Position Surface form Disambiguated ID Type / Status
Subject Dijon-Bourgogne Airport E358867 entity
Predicate adjacentTo P224 FINISHED
Object Longvic
Longvic is a commune in eastern France’s Côte-d’Or department, forming part of the suburban area just southeast of the city of Dijon.
E1100243 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: Longvic | Statement: [Dijon-Bourgogne Airport, adjacentTo, Longvic]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Longvic
Context triple: [Dijon-Bourgogne Airport, adjacentTo, Longvic]
  • A. Eiffage
    Eiffage is a major French construction and civil engineering company known for delivering large-scale infrastructure projects such as the Millau Viaduct.
  • B. Vinci Construction
    Vinci Construction is a major global construction and civil engineering company specializing in large-scale infrastructure and building projects.
  • C. Vinci SA
    Vinci SA is a major French multinational concessions and construction company specializing in infrastructure development and management worldwide.
  • D. Dampierre
    Dampierre is a small French commune located in the Aube department in the Grand Est region of north-central France.
  • E. Saint-Gobain
    Saint-Gobain is a major French multinational corporation specializing in the production and distribution of construction materials and high-performance solutions for buildings and industry.
  • 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: Longvic
Triple: [Dijon-Bourgogne Airport, adjacentTo, Longvic]
Generated description
Longvic is a commune in eastern France’s Côte-d’Or department, forming part of the suburban area just southeast of the city of Dijon.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Longvic
Target entity description: Longvic is a commune in eastern France’s Côte-d’Or department, forming part of the suburban area just southeast of the city of Dijon.
  • A. Eiffage
    Eiffage is a major French construction and civil engineering company known for delivering large-scale infrastructure projects such as the Millau Viaduct.
  • B. Vinci Construction
    Vinci Construction is a major global construction and civil engineering company specializing in large-scale infrastructure and building projects.
  • C. Vinci SA
    Vinci SA is a major French multinational concessions and construction company specializing in infrastructure development and management worldwide.
  • D. Dampierre
    Dampierre is a small French commune located in the Aube department in the Grand Est region of north-central France.
  • E. Saint-Gobain
    Saint-Gobain is a major French multinational corporation specializing in the production and distribution of construction materials and high-performance solutions for buildings and industry.
  • 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_69d827966698819082e140837737501d completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de91fab21c819090b6e209d8efba6e completed April 14, 2026, 7:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd649e103c81908001b45c16d1fd79 completed May 8, 2026, 4:20 a.m.
NEDg Description generation batch_69fd658f2c1c8190b6a564dbe75fc2f2 completed May 8, 2026, 4:24 a.m.
NED2 Entity disambiguation (via description) batch_69fd661c201c8190ba8ce1295849e8c1 completed May 8, 2026, 4:27 a.m.
Created at: April 10, 2026, 1:20 a.m.