Triple
T17592159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gare de Caen |
E428473
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | SNCF |
—
|
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: SNCF | Statement: [Gare de Caen, operator, SNCF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SNCF Context triple: [Gare de Caen, operator, SNCF]
-
A.
SNCF
chosen
SNCF is France’s national state-owned railway company, responsible for operating the country’s passenger and freight rail services and much of its rail infrastructure.
-
B.
SNCF Réseau
SNCF Réseau is the French state-owned rail infrastructure manager responsible for operating, maintaining, and developing France’s national railway network.
-
C.
SNCF Sud-Est region
The SNCF Sud-Est region was a major operating division of the French national railway company responsible for managing and running rail services in the southeastern part of France, including key routes linking Paris with Lyon and the Mediterranean.
-
D.
OUIGO
OUIGO is a low-cost high-speed train service operated by France’s national railway company SNCF, offering budget-friendly travel on major routes.
-
E.
Francorail
Francorail was a French railway manufacturing consortium known for producing high-speed trainsets, including early models of the TGV.
- 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469e79dac8190953a1ce8fc015b20 |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 5:51 a.m.