Triple
T19431398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valenciennes tramway |
E486120
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Valenciennes station
Valenciennes station is a key tram stop on the Valenciennes tramway network in northern France, serving as an important public transport hub for the city.
|
E1376698
|
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: Valenciennes station | Statement: [Valenciennes tramway, hasStation, Valenciennes station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valenciennes station Context triple: [Valenciennes tramway, hasStation, Valenciennes station]
-
A.
Reims-Maison-Blanche station
Reims-Maison-Blanche station is a local railway station serving the city of Reims in northeastern France.
-
B.
Vaucelles station
Vaucelles station is a railway station serving the commune of Taverny in the northern suburbs of Paris, France.
-
C.
Troyes station
Troyes station is the main railway station serving the city of Troyes in northeastern France, providing regional and intercity train connections.
-
D.
Reims station
Reims station is the main railway station serving the city of Reims in northeastern France, providing regional and high-speed train connections.
-
E.
Foch station
Foch station is an underground metro stop on the Lyon Metro network in Lyon, France, serving passengers on line A.
- 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: Valenciennes station Triple: [Valenciennes tramway, hasStation, Valenciennes station]
Generated description
Valenciennes station is a key tram stop on the Valenciennes tramway network in northern France, serving as an important public transport hub for the city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Valenciennes station Target entity description: Valenciennes station is a key tram stop on the Valenciennes tramway network in northern France, serving as an important public transport hub for the city.
-
A.
Reims-Maison-Blanche station
Reims-Maison-Blanche station is a local railway station serving the city of Reims in northeastern France.
-
B.
Vaucelles station
Vaucelles station is a railway station serving the commune of Taverny in the northern suburbs of Paris, France.
-
C.
Troyes station
Troyes station is the main railway station serving the city of Troyes in northeastern France, providing regional and intercity train connections.
-
D.
Reims station
Reims station is the main railway station serving the city of Reims in northeastern France, providing regional and high-speed train connections.
-
E.
Foch station
Foch station is an underground metro stop on the Lyon Metro network in Lyon, France, serving passengers on line A.
- 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6335b4e388190913ded15ad165b7b |
completed | April 20, 2026, 2:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a073b269ca481908ecc68cfdd4c5d61 |
completed | May 15, 2026, 3:26 p.m. |
| NEDg | Description generation | batch_6a073c0a02c8819080b0e71caf65e6fa |
completed | May 15, 2026, 3:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a073d0e92d48190880317263fe6e481 |
completed | May 15, 2026, 3:34 p.m. |
Created at: April 10, 2026, 1:37 p.m.