Triple

T7423514
Position Surface form Disambiguated ID Type / Status
Subject Versoix railway station E171305 entity
Predicate fareZoneSystem P395 FINISHED
Object Unireso E127786 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: Unireso | Statement: [Versoix railway station, fareZoneSystem, Unireso]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Unireso
Context triple: [Versoix railway station, fareZoneSystem, Unireso]
  • A. unireso chosen
    unireso is the integrated public transport fare network for the Geneva region, coordinating tickets and tariffs across multiple operators and modes of transport.
  • B. Osan University
    Osan University is a higher education institution located in Osan, a city in Gyeonggi Province, South Korea.
  • C. Todai
    Todai is the common nickname for the University of Tokyo, Japan’s most prestigious and influential national research university.
  • D. Hoshi University
    Hoshi University is a private Japanese university in Tokyo known for its specialized programs in pharmaceutical sciences and related health fields.
  • E. Misurata University
    Misurata University is a public higher education institution in the city of Misrata, Libya, offering a range of undergraduate and postgraduate programs across multiple disciplines.
  • 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_69c68a625d048190af70eb8b63bec5a0 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f2eece588190905774e7151edcb8 completed March 27, 2026, 9:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69c81effc488819086336eea92604fa8 completed March 28, 2026, 6:33 p.m.
Created at: March 27, 2026, 3:12 p.m.