Triple
T8788446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TER Nouvelle-Aquitaine |
E209099
|
entity |
| Predicate | connectsWith |
P37
|
FINISHED |
| Object | TGV inOui |
E246182
|
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 inOui | Statement: [TER Nouvelle-Aquitaine, connectsWith, TGV inOui]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TGV inOui Context triple: [TER Nouvelle-Aquitaine, connectsWith, TGV inOui]
-
A.
TGV inOui
chosen
TGV inOui is SNCF’s premium high-speed train service in France, offering upgraded comfort and amenities on major routes including those served by the LGV Méditerranée line.
-
B.
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.
-
C.
TGV PSE
TGV PSE is the original generation of French high-speed TGV Sud-Est trainsets that inaugurated high-speed rail service in 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.
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.
- 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_69cfc921d3408190a2f823473bf9b4bc |
completed | April 3, 2026, 2:05 p.m. |
Created at: March 30, 2026, 6:43 p.m.