Triple
T8788448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TER Nouvelle-Aquitaine |
E209099
|
entity |
| Predicate | connectsWith |
P37
|
FINISHED |
| Object | Intercités de nuit |
E39984
|
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: Intercités de nuit | Statement: [TER Nouvelle-Aquitaine, connectsWith, Intercités de nuit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Intercités de nuit Context triple: [TER Nouvelle-Aquitaine, connectsWith, Intercités de nuit]
-
A.
Intercités
chosen
Intercités is a network of French long-distance conventional trains operated by SNCF, connecting major cities and regions across the country.
-
B.
Le Train Bleu
Le Train Bleu is a famous historic restaurant in Paris’s Gare de Lyon, renowned for its opulent Belle Époque decor and classic French cuisine.
-
C.
Bezannes TGV
Bezannes TGV is a tram terminus and transport hub in the suburb of Bezannes serving the high-speed TGV rail connections near Reims, France.
-
D.
TGV
TGV is France’s high-speed intercity train service, renowned for rapid connections between major cities such as Paris and Lille.
-
E.
SNCF Voyageurs
SNCF Voyageurs is the passenger rail operating division of France’s national railway company, responsible for running high-speed, regional, and commuter train services.
- 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_69ca836168108190bb43d3dc235c1f55 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f89a84c819085d4cfe4e6dfbda8 |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf5210454c8190aa83d941893a4bc5 |
completed | April 3, 2026, 5:37 a.m. |
Created at: March 30, 2026, 6:43 p.m.