Triple

T1816795
Position Surface form Disambiguated ID Type / Status
Subject École des Ponts et Chaussées E40452 entity
Predicate hasAlumni P51 FINISHED
Object Michel Virlogeux E287257 NE FINISHED

How this triple was built (2 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: Michel Virlogeux | Statement: [École des Ponts et Chaussées, hasAlumni, Michel Virlogeux]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michel Virlogeux
Context triple: [École des Ponts et Chaussées, hasAlumni, Michel Virlogeux]
  • A. Michel Virlogeux chosen
    Michel Virlogeux is a renowned French structural engineer and bridge designer known for his work on major long-span bridges around the world.
  • B. Thierry Delaporte
    Thierry Delaporte is a French business executive best known as the CEO and Managing Director of Indian IT services giant Wipro.
  • C. Michel Andrault
    Michel Andrault was a prominent French architect known for his influential large-scale housing and urban development projects in the late 20th century.
  • D. Thierry Cruanes
    Thierry Cruanes is a computer scientist and entrepreneur best known as a co-founder of the cloud data warehousing company Snowflake.
  • E. Louis Boisot
    Louis Boisot was a Dutch nobleman and admiral of the Sea Beggars who played a key role in the Dutch Revolt by helping to relieve the besieged city of Leiden in 1574.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a8864526c081908a3a4d74f689e2c5 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa65f614888190a475f7df627d5f0a completed March 6, 2026, 5:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf15a4248190b898e3bfbeb2997a completed March 10, 2026, 5:41 a.m.
Created at: March 4, 2026, 7:32 p.m.