Triple

T1919318
Position Surface form Disambiguated ID Type / Status
Subject Jean-Étienne-Marie Portalis E40088 entity
Predicate placeOfBirth P1 FINISHED
Object Var E84932 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: Var | Statement: [Jean-Étienne-Marie Portalis, placeOfBirth, Var]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Var
Context triple: [Jean-Étienne-Marie Portalis, placeOfBirth, Var]
  • A. Var chosen
    Var is a department in southeastern France known for its Mediterranean coastline, including popular resort areas along the French Riviera.
  • B. VAR
    VAR (Video Assistant Referee) is a football officiating system that uses video technology to help referees review and correct clear and obvious errors in key match situations.
  • C. Varig
    Varig was Brazil’s former flagship airline, once the country’s largest carrier and a major international operator throughout much of the 20th century.
  • D. Vara
    Vara is a short form of the female given name Varvara, commonly used in Slavic languages.
  • E. Val
    Val is the Allied reporting name for the Aichi D3A, a Japanese World War II carrier-based dive bomber used prominently in early Pacific naval battles.
  • 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_69a8864298748190a2f2fd34f7ef8d77 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb211eda88190865de7a0522a453d completed March 7, 2026, 5:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3df3fdc819095cfdf508e8bdb22 completed March 8, 2026, 10:10 p.m.
Created at: March 4, 2026, 7:35 p.m.