Triple
T3141549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RER B |
E65656
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Gare du Nord |
E6061
|
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: Gare du Nord | Statement: [RER B, passesThrough, Gare du Nord]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gare du Nord Context triple: [RER B, passesThrough, Gare du Nord]
-
A.
Gare du Nord
chosen
Gare du Nord is one of Europe's busiest railway stations in Paris, serving as a major hub for domestic and international train services, including routes to northern France, the UK, and other parts of Europe.
-
B.
Gare de l'Est
Gare de l'Est is one of Paris's major railway stations, serving eastern France and international destinations such as Germany and Luxembourg.
-
C.
Gare de Lyon
Gare de Lyon is one of Paris’s major railway terminals, serving high-speed and regional trains to southeastern France and international destinations.
-
D.
Gare Montparnasse
Gare Montparnasse is one of Paris’s major railway terminals, serving primarily southwestern France and known for its modern complex and historic 1895 train crash.
-
E.
Paris-Saint-Lazare station
Paris-Saint-Lazare station is one of the main railway termini in Paris, serving as a major hub for suburban and regional trains in the western part of the Île-de-France region.
- 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_69ad8582f564819088c27e1f96153938 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada57895dc8190bd3d4ef9391973dc |
completed | March 8, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b57f0432e081908110ede1c2b7a54a |
completed | March 14, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:05 p.m.