Triple
T8304090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line R |
E194417
|
entity |
| Predicate | terminus |
P388
|
FINISHED |
| Object | Paris-Gare-de-Lyon |
E6964
|
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: Paris-Gare-de-Lyon | Statement: [Line R, terminus, Paris-Gare-de-Lyon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paris-Gare-de-Lyon Context triple: [Line R, terminus, Paris-Gare-de-Lyon]
-
A.
Gare de Lyon
chosen
Gare de Lyon is one of Paris’s major railway terminals, serving high-speed and regional trains to southeastern France and international destinations.
-
B.
Lyon station
Lyon station is an underground light rail transit station in downtown Ottawa, Canada, serving the city’s O-Train Confederation Line.
-
C.
Lyon-Perrache station
Lyon-Perrache station is one of Lyon’s main railway hubs, serving regional, national, and international train connections in southeastern France.
-
D.
Lyon-Part-Dieu railway station
Lyon-Part-Dieu railway station is a major high-speed and regional rail hub in Lyon, France, serving as one of the country’s busiest and most important transport interchanges.
-
E.
Marseille-Saint-Charles station
Marseille-Saint-Charles station is the main railway terminus in Marseille, France, serving as a major national and regional transport hub.
- 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_69ca82e613e88190bf8139669bbd0d53 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7e8b9f6081909100d1da8a078616 |
completed | March 31, 2026, 7:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cecc2137c88190ad5949df5a7487f2 |
completed | April 2, 2026, 8:05 p.m. |
Created at: March 30, 2026, 5:53 p.m.