Triple
T19846584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gare de Tarbes |
E476875
|
entity |
| Predicate | hasService |
P182
|
FINISHED |
| Object | Intercités de nuit |
—
|
NE NERFINISHED |
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: [Gare de Tarbes, hasService, Intercités de nuit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Intercités de nuit Context triple: [Gare de Tarbes, hasService, 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.
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.
-
D.
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.
-
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 (2 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65809da2c8190bb579ef42513b74d |
completed | April 20, 2026, 4:44 p.m. |
Created at: April 10, 2026, 1:51 p.m.