Triple

T3393721
Position Surface form Disambiguated ID Type / Status
Subject Vandoeuvres E71477 entity
Predicate hasNeighbouringMunicipality P224 FINISHED
Object Meinier E165808 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: Meinier | Statement: [Vandoeuvres, hasNeighbouringMunicipality, Meinier]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Meinier
Context triple: [Vandoeuvres, hasNeighbouringMunicipality, Meinier]
  • A. Meinier chosen
    Meinier is a rural municipality in the canton of Geneva, Switzerland, known for its agricultural landscape and proximity to the city of Geneva.
  • B. Balderas
    Balderas is a major Mexico City Metro station known for its central location and high passenger traffic.
  • C. Montero Ríos
    Montero Ríos is the surname of Eugenio Montero Ríos, a prominent Spanish jurist and politician who served as Prime Minister of Spain in the early 20th century.
  • D. Magaña
    Magaña is a Spanish-language surname of Hispanic origin borne by various notable individuals in Mexico and other Spanish-speaking countries.
  • E. Cansino
    Cansino is the original family surname of Hollywood actress and dancer Rita Hayworth, reflecting her Spanish heritage.
  • 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_69ad85a9c4a88190a854019341cb3b60 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb853746c8190bfa1447e6ebbefb3 completed March 8, 2026, 5:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b34bc8a75c8190ab4f652272d33576 completed March 12, 2026, 11:27 p.m.
Created at: March 8, 2026, 3:14 p.m.