Triple

T15951913
Position Surface form Disambiguated ID Type / Status
Subject TGV V150 E386837 entity
Predicate basedOn P98 FINISHED
Object TGV Duplex E90446 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 Duplex | Statement: [TGV V150, basedOn, TGV Duplex]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TGV Duplex
Context triple: [TGV V150, basedOn, TGV Duplex]
  • A. TGV PSE
    TGV PSE is the original generation of French high-speed TGV Sud-Est trainsets that inaugurated high-speed rail service in France.
  • B. TGV INOUI
    TGV INOUI is SNCF’s premium high-speed train service in France, offering upgraded comfort, amenities, and service compared to standard TGV trains.
  • C. TGV Duplex trainset chosen
    The TGV Duplex trainset is a high-speed, double-decker French train designed to carry large numbers of passengers efficiently on long-distance routes.
  • D. 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.
  • E. TGV
    TGV is France’s high-speed intercity train service, renowned for rapid connections between major cities such as Paris and Lille.
  • 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_69d86da882448190a82ea962fe343b79 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e156d59f5081909f6a81d578c4e2e8 completed April 16, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffbe7ab070819090232efdeecdd5fb completed May 9, 2026, 11:08 p.m.
Created at: April 10, 2026, 4:53 a.m.