Triple
T4909573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lille-Europe station |
E110198
|
entity |
| Predicate | servesTrainOperator |
P782
|
FINISHED |
| Object | SNCF TGV |
E445505
|
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: SNCF TGV | Statement: [Lille-Europe station, servesTrainOperator, SNCF TGV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SNCF TGV Context triple: [Lille-Europe station, servesTrainOperator, SNCF TGV]
-
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 Sud-Est trainset
The TGV Sud-Est trainset is the original high-speed electric multiple unit used on France’s pioneering TGV services, known for inaugurating high-speed rail in the country in the early 1980s.
-
C.
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.
-
D.
TGV
chosen
TGV is France’s high-speed intercity train service, renowned for rapid connections between major cities such as Paris and Lille.
-
E.
TGV Duplex trainset
The TGV Duplex trainset is a high-speed, double-decker French train designed to carry large numbers of passengers efficiently on long-distance routes.
- 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_69bd44132b94819088522d92beaadc78 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e99414081908c3d3283f563bba4 |
completed | March 20, 2026, 3:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be6fe43a888190ab1b150da0f49203 |
completed | March 21, 2026, 10:16 a.m. |
Created at: March 20, 2026, 1:29 p.m.