Triple

T4255602
Position Surface form Disambiguated ID Type / Status
Subject Alpes-de-Haute-Provence E95965 entity
Predicate borders P224 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: [Alpes-de-Haute-Provence, borders, Var]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Var
Context triple: [Alpes-de-Haute-Provence, borders, 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 is a Norse goddess associated with oaths, agreements, and the punishment of those who break them.
  • C. 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.
  • D. 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.
  • E. Vari
    Vari is a settlement on the Greek island of Syros, known for its coastal location and beaches in the Cyclades.
  • 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_69b3453f759881909b91f01a1e82c036 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34ec1971c81908f7a72418efa8bcc completed March 12, 2026, 11:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a88a68f081909e5bae5b0414f534 completed March 14, 2026, 6:27 p.m.
Created at: March 12, 2026, 11:06 p.m.