Triple

T10884900
Position Surface form Disambiguated ID Type / Status
Subject Léonce Verny E257016 entity
Predicate familyName P18 FINISHED
Object Verny
Verny is the surname of Léonce Verny, a 19th-century French engineer known for helping establish Japan’s modern naval shipyards and industrial infrastructure.
 E890547 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: Verny | Statement: [Léonce Verny, familyName, Verny]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Verny
Context triple: [Léonce Verny, familyName, Verny]
  • A. Verny
    Verny was the historical name of the city now known as Almaty, a major cultural and economic center in Kazakhstan.
  • B. Valette
    Valette is a French surname most notably associated with Pierre-Adolphe Valette, an influential early 20th-century Impressionist painter and teacher in Manchester.
  • C. Vallette
    Vallette is a district in Turin, Italy, known for hosting the modern Allianz Stadium, home of the Juventus football club.
  • D. Vauvert
    Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
  • E. Vénéon
    Vénéon is a mountain river in the French Alps known for its glacial waters and scenic valley in the Écrins massif.
  • 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: Verny
Triple: [Léonce Verny, familyName, Verny]
Generated description
Verny is the surname of Léonce Verny, a 19th-century French engineer known for helping establish Japan’s modern naval shipyards and industrial infrastructure.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Verny
Target entity description: Verny is the surname of Léonce Verny, a 19th-century French engineer known for helping establish Japan’s modern naval shipyards and industrial infrastructure.
  • A. Verny
    Verny was the historical name of the city now known as Almaty, a major cultural and economic center in Kazakhstan.
  • B. Valette
    Valette is a French surname most notably associated with Pierre-Adolphe Valette, an influential early 20th-century Impressionist painter and teacher in Manchester.
  • C. Vallette
    Vallette is a district in Turin, Italy, known for hosting the modern Allianz Stadium, home of the Juventus football club.
  • D. Vauvert
    Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
  • E. Vénéon
    Vénéon is a mountain river in the French Alps known for its glacial waters and scenic valley in the Écrins massif.
  • 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_69d6aa848804819081b2713ca0bedf06 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d751dd6a3c81909965ef774e8b7309 completed April 9, 2026, 7:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69dff7ecf1c48190aef0d31ef03d1f88 completed April 15, 2026, 8:41 p.m.
NEDg Description generation batch_69e002709d38819099c4402d30824612 completed April 15, 2026, 9:26 p.m.
NED2 Entity disambiguation (via description) batch_69e005873ba48190b8c24c77611562fa completed April 15, 2026, 9:39 p.m.
Created at: April 8, 2026, 9:21 p.m.