Triple

T1151496
Position Surface form Disambiguated ID Type / Status
Subject CAC 40 E23686 entity
Predicate hasComponent P35 FINISHED
Object Vinci
Vinci is a major French concessions and construction company and one of the largest infrastructure and engineering groups in the world.
E131991 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: Vinci | Statement: [CAC 40, hasComponent, Vinci]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vinci
Context triple: [CAC 40, hasComponent, Vinci]
  • A. Vinci
    Vinci is a small Tuscan town in Italy best known as the birthplace of Renaissance polymath Leonardo da Vinci.
  • B. Leonardo
    Leonardo is the first name of Leonardo DiCaprio, the acclaimed American actor and environmental activist known for films such as Titanic and Inception.
  • C. Cour Carrée
    Cour Carrée is the large, historic square courtyard at the eastern end of the Louvre in Paris, surrounded by classical palace façades that reflect the museum’s origins as a royal residence.
  • D. Vauban
    Vauban was a renowned 17th-century French military engineer and Marshal of France, famous for revolutionizing fortification design and siege warfare under Louis XIV.
  • E. de Bèze
    De Bèze is the French family name of Théodore de Bèze (Theodore Beza), a prominent 16th-century Protestant Reformer and successor to John Calvin in Geneva.
  • 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: Vinci
Triple: [CAC 40, hasComponent, Vinci]
Generated description
Vinci is a major French concessions and construction company and one of the largest infrastructure and engineering groups in the world.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Vinci
Target entity description: Vinci is a major French concessions and construction company and one of the largest infrastructure and engineering groups in the world.
  • A. Vinci
    Vinci is a small Tuscan town in Italy best known as the birthplace of Renaissance polymath Leonardo da Vinci.
  • B. Leonardo
    Leonardo is the first name of Leonardo DiCaprio, the acclaimed American actor and environmental activist known for films such as Titanic and Inception.
  • C. Cour Carrée
    Cour Carrée is the large, historic square courtyard at the eastern end of the Louvre in Paris, surrounded by classical palace façades that reflect the museum’s origins as a royal residence.
  • D. Vauban
    Vauban was a renowned 17th-century French military engineer and Marshal of France, famous for revolutionizing fortification design and siege warfare under Louis XIV.
  • E. de Bèze
    De Bèze is the French family name of Théodore de Bèze (Theodore Beza), a prominent 16th-century Protestant Reformer and successor to John Calvin in Geneva.
  • 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_69a493f0d32c8190ac74bad3c87f2641 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc744e7c81908f8612f2aad28600 completed March 1, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac5eb5d36c8190916a43a5f41df144 completed March 7, 2026, 5:21 p.m.
NEDg Description generation batch_69ac5f4756b08190b3dbaf64a9351836 completed March 7, 2026, 5:24 p.m.
NED2 Entity disambiguation (via description) batch_69ac600f78148190bd3109276f7b9e3a completed March 7, 2026, 5:27 p.m.
Created at: March 1, 2026, 7:44 p.m.