Triple
T8456939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London–Paris |
E199941
|
entity |
| Predicate | primaryOperator |
P5623
|
FINISHED |
| Object | Eurostar |
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 | Statement: [London–Paris, primaryOperator, Eurostar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eurostar Context triple: [London–Paris, primaryOperator, Eurostar]
-
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.
Thalys
Thalys is a high-speed international train service connecting major cities in France, Belgium, the Netherlands, and Germany.
-
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.
TGV
TGV is France’s high-speed intercity train service, renowned for rapid connections between major cities such as Paris and Lille.
-
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_69ca8318231881908fd1bc1c4d45d286 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe48f180c8190a71cf9d7248ade60 |
completed | March 31, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebb52978481909c60e43abd496746 |
completed | April 2, 2026, 6:54 p.m. |
Created at: March 30, 2026, 6:10 p.m.