Triple
T8445132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Cologne |
E199653
|
entity |
| Predicate | usesRollingStock |
P5426
|
FINISHED |
| Object | 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: TGV | Statement: [Paris–Cologne, usesRollingStock, TGV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TGV Context triple: [Paris–Cologne, usesRollingStock, TGV]
-
A.
TGV
chosen
TGV is France’s high-speed intercity train service, renowned for rapid connections between major cities such as Paris and Lille.
-
B.
TGV Est
TGV Est is a French high-speed train service connecting Paris with eastern France and neighboring European countries such as Germany, Luxembourg, and Switzerland.
-
C.
TGV Ouigo
TGV Ouigo is a low-cost high-speed train service operated by SNCF in France, offering budget fares on selected TGV routes.
-
D.
TGV PSE
TGV PSE is the original generation of French high-speed TGV Sud-Est trainsets that inaugurated high-speed rail service in France.
-
E.
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.
- 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_69ca83170f9081909cd98f55614c6476 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe3138ee08190918cd82adbe2d9a1 |
completed | March 31, 2026, 3:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1dac8cb08190b74985a6ba3c938f |
completed | April 2, 2026, 7:41 a.m. |
Created at: March 30, 2026, 6:09 p.m.