Triple
T3241782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gare de Strasbourg |
E67979
|
entity |
| Predicate | hasService |
P182
|
FINISHED |
| Object | TGV |
E11761
|
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: [Gare de Strasbourg, hasService, TGV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TGV Context triple: [Gare de Strasbourg, hasService, TGV]
-
A.
TGV Ouigo
TGV Ouigo is a low-cost high-speed train service operated by SNCF in France, offering budget fares on selected TGV routes.
-
B.
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.
-
C.
TGV high-speed rail
chosen
TGV high-speed rail is France’s flagship high-speed train service that connects major cities and hubs, including direct links from Charles de Gaulle Airport to destinations across the country and into neighboring nations.
-
D.
TGV inOui
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.
-
E.
Intercités
Intercités is a network of French long-distance conventional trains operated by SNCF, connecting major cities and regions across the country.
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaef76d908190815bb456e366ee0a |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28eadeff481909bd48cdf51044f86 |
completed | March 12, 2026, 10 a.m. |
Created at: March 8, 2026, 3:08 p.m.