Triple

T5573454
Position Surface form Disambiguated ID Type / Status
Subject Basel SBB E146258 entity
Predicate servesOperator P5884 FINISHED
Object TGV Lyria E402577 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: TGV Lyria | Statement: [Basel SBB, servesOperator, TGV Lyria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TGV Lyria
Context triple: [Basel SBB, servesOperator, TGV Lyria]
  • A. TGV Lyria chosen
    TGV Lyria is a high-speed train service linking France and Switzerland, operated as a joint venture between SNCF and Swiss Federal Railways.
  • B. TGV Est
    TGV Est is a French high-speed train service connecting Paris with eastern France and neighboring European countries such as Germany, Luxembourg, and Switzerland.
  • C. TGV
    TGV is France’s high-speed intercity train service, renowned for rapid connections between major cities such as Paris and Lille.
  • D. TGV Ouigo
    TGV Ouigo is a low-cost high-speed train service operated by SNCF in France, offering budget fares on selected TGV routes.
  • E. TGV Réseau
    TGV Réseau is a later-generation French high-speed trainset used by SNCF, designed for improved performance and comfort on the expanding TGV network.
  • 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_69c008ffed108190a084602227af6157 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c02052dd0481909aba6863831357eb completed March 22, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69c02852a6fc8190a543508ab3237f95 completed March 22, 2026, 5:35 p.m.
Created at: March 22, 2026, 3:37 p.m.