Triple
T10541126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TGV Ouigo |
E248696
|
entity |
| Predicate | notableRoute |
P22
|
FINISHED |
| Object |
Paris–Nantes
Paris–Nantes is a major high-speed rail corridor in France linking the capital Paris with the western city of Nantes.
|
E873647
|
NE FINISHED |
How this triple was built (4 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–Nantes | Statement: [TGV Ouigo, notableRoute, Paris–Nantes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paris–Nantes Context triple: [TGV Ouigo, notableRoute, Paris–Nantes]
-
A.
Paris–Marseille
Paris–Marseille is a major French intercity rail corridor linking the capital Paris with the Mediterranean port city of Marseille.
-
B.
Paris–Bordeaux
Paris–Bordeaux is a major high-speed rail corridor in France connecting the capital with the southwest, known for its fast TGV services.
-
C.
Paris–Granville
Paris–Granville is a regional railway service in France connecting the capital city Paris with the coastal town of Granville in Normandy.
-
D.
Paris–Lille
Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
-
E.
Paris–Rennes
Paris–Rennes is a major high-speed rail corridor in France linking the capital Paris with the city of Rennes in Brittany.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Paris–Nantes Triple: [TGV Ouigo, notableRoute, Paris–Nantes]
Generated description
Paris–Nantes is a major high-speed rail corridor in France linking the capital Paris with the western city of Nantes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paris–Nantes Target entity description: Paris–Nantes is a major high-speed rail corridor in France linking the capital Paris with the western city of Nantes.
-
A.
Paris–Marseille
Paris–Marseille is a major French intercity rail corridor linking the capital Paris with the Mediterranean port city of Marseille.
-
B.
Paris–Bordeaux
Paris–Bordeaux is a major high-speed rail corridor in France connecting the capital with the southwest, known for its fast TGV services.
-
C.
Paris–Granville
Paris–Granville is a regional railway service in France connecting the capital city Paris with the coastal town of Granville in Normandy.
-
D.
Paris–Lille
Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
-
E.
Paris–Rennes
Paris–Rennes is a major high-speed rail corridor in France linking the capital Paris with the city of Rennes in Brittany.
- F. None of above. chosen
Provenance (5 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50a5918648190b16c2d1bc1bf015f |
completed | April 7, 2026, 1:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95e68d1288190920c26cbfd396a21 |
completed | April 10, 2026, 8:32 p.m. |
| NEDg | Description generation | batch_69d95f80d0c48190b88e3a4b3e42279c |
completed | April 10, 2026, 8:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d9602748608190b0c971accf44b7aa |
completed | April 10, 2026, 8:40 p.m. |
Created at: April 6, 2026, 12:32 p.m.