Triple
T9197452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Channel Tunnel Rail Link |
E220751
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Eurostar services |
E39296
|
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: Eurostar services | Statement: [Channel Tunnel Rail Link, serves, Eurostar services]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eurostar services Context triple: [Channel Tunnel Rail Link, serves, Eurostar services]
-
A.
Eurostar
chosen
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.
-
B.
TGV services
TGV services are France’s high-speed train operations, providing fast intercity and international rail connections across the country and beyond.
-
C.
Thalys
Thalys is a high-speed international train service connecting major cities in France, Belgium, the Netherlands, and Germany.
-
D.
TGV Atlantique services
TGV Atlantique services are high-speed French train operations running primarily between Paris and western or southwestern France using TGV Atlantique trainsets.
-
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 (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_69ca83e7ba70819088b74866d9da2c30 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd87f50e88190940e73af1deda747 |
completed | April 1, 2026, 8:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d05c3b1af48190bb03af15232c510d |
completed | April 4, 2026, 12:32 a.m. |
Created at: March 30, 2026, 7:25 p.m.