Triple

T4855657
Position Surface form Disambiguated ID Type / Status
Subject France and Switzerland E108530 entity
Predicate haveCrossBorderCommuting P39561 FINISHED
Object yes LITERAL 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: yes | Statement: [France and Switzerland, haveCrossBorderCommuting, yes]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: haveCrossBorderCommuting
Context triple: [France and Switzerland, haveCrossBorderCommuting, yes]
  • A. hasCrossBorderCommuting chosen
    Indicates that there is a regular pattern of commuting across national borders between the related entities.
  • B. hasBorderCrossing
    Indicates that there exists a point or facility where movement or transit is possible between the boundaries of two adjacent regions or jurisdictions.
  • C. hasBorderCrossingFunction
    Indicates that an entity serves as a location or facility where people, goods, or vehicles can legally cross a border between jurisdictions.
  • D. hasCrossBorderManagement
    Indicates that an entity exercises management or control over operations, assets, or activities that extend across national borders.
  • E. hasCrossBorderTourism
    Indicates that there is tourism activity involving travel across national or regional borders between the related entities.
  • F. None of above.

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_69bd440a89548190a5f14ba6da6b97dc completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6ddd17d881909f7731ff2b460e83 completed March 20, 2026, 3:55 p.m.
PD Predicate disambiguation batch_69bd6c2557388190a2d15571bacd24f3 completed March 20, 2026, 3:47 p.m.
Created at: March 20, 2026, 1:26 p.m.