Triple
T15951967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TVM-430 |
E386838
|
entity |
| Predicate | appliesTo |
P1129
|
FINISHED |
| Object | Thalys trains |
E38855
|
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: Thalys trains | Statement: [TVM-430, appliesTo, Thalys trains]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thalys trains Context triple: [TVM-430, appliesTo, Thalys trains]
-
A.
Thalys
chosen
Thalys is a high-speed international train service connecting major cities in France, Belgium, the Netherlands, and Germany.
-
B.
Eurostar
Eurostar is a high-speed international train service connecting the United Kingdom with mainland Europe via the Channel Tunnel, linking cities such as London, Paris, and Brussels.
-
C.
TGV Lyria
TGV Lyria is a high-speed train service linking France and Switzerland, operated as a joint venture between SNCF and Swiss Federal Railways.
-
D.
EuroCity trains
EuroCity trains are a network of high-quality international express passenger services that connect major cities across European countries with fast, comfortable, and cross-border rail travel.
-
E.
SNCF Connect
SNCF Connect is the official digital platform and app of the French national railway company, providing online ticket booking, travel planning, and real-time information for trains and other transport services.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156d59f5081909f6a81d578c4e2e8 |
completed | April 16, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3c1b49c819087e5a088d41963ec |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:53 a.m.