Triple
T10847381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Rennes railway |
E256048
|
entity |
| Predicate | usedByService |
P1294
|
FINISHED |
| Object | TER Bretagne |
E206986
|
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: TER Bretagne | Statement: [Paris–Rennes railway, usedByService, TER Bretagne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TER Bretagne Context triple: [Paris–Rennes railway, usedByService, TER Bretagne]
-
A.
TER Bretagne
chosen
TER Bretagne is the regional rail network operated by SNCF that provides passenger train services throughout the Brittany region of France.
-
B.
Marine en Bretagne
Marine en Bretagne is a seascape painting by French Post-Impressionist artist Maxime Maufra, depicting the rugged coastal scenery of Brittany.
-
C.
La côte de Bretagne
La côte de Bretagne is a landscape painting by French artist Maxime Maufra depicting the rugged, windswept coastline of Brittany.
-
D.
Le Breton
Le Breton is a French surname borne by various notable figures, including publishers, politicians, and artists.
-
E.
TER Basse-Normandie
TER Basse-Normandie was a regional rail network serving the Basse-Normandie area of France as part of the national TER (Transport Express Régional) system.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75113bc188190ac78df0c51d95de6 |
completed | April 9, 2026, 7:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb170e714819097babb2b850342d2 |
completed | April 14, 2026, 9:28 p.m. |
Created at: April 8, 2026, 9:20 p.m.