Triple
T17810665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TGV PSE |
E444692
|
entity |
| Predicate | primaryRoute |
P6298
|
FINISHED |
| Object | Paris–Lyon |
—
|
NE NERFINISHED |
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–Lyon | Statement: [TGV PSE, primaryRoute, Paris–Lyon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paris–Lyon Context triple: [TGV PSE, primaryRoute, Paris–Lyon]
-
A.
Paris–Lyon
chosen
Paris–Lyon is the pioneering high-speed rail corridor in France that became the flagship route for TGV operations between the capital and the major southeastern city.
-
B.
Paris–Marseille
Paris–Marseille is a major French intercity rail corridor linking the capital Paris with the Mediterranean port city of Marseille.
-
C.
Paris–Dijon
Paris–Dijon is a major French high-speed rail corridor linking the capital Paris with the historic city of Dijon, serving as part of international routes toward Switzerland and beyond.
-
D.
Paris–Strasbourg
Paris–Strasbourg is a major high-speed rail corridor in France linking the capital with the Alsatian city near the German border.
-
E.
Paris–Nantes
Paris–Nantes is a major high-speed rail corridor in France linking the capital Paris with the western city of Nantes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4887a50488190b9c148146ec607e6 |
completed | April 19, 2026, 7:47 a.m. |
Created at: April 10, 2026, 10:14 a.m.