Triple
T18449659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Courchavon |
E450745
|
entity |
| Predicate | hasRailwayStation |
P918
|
FINISHED |
| Object |
Courchavon railway station
Courchavon railway station is a small regional train stop in the municipality of Courchavon in the Swiss canton of Jura.
|
E1325602
|
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: Courchavon railway station | Statement: [Courchavon, hasRailwayStation, Courchavon railway station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Courchavon railway station Context triple: [Courchavon, hasRailwayStation, Courchavon railway station]
-
A.
Taverny station
Taverny station is a suburban railway stop in the Val-d'Oise department of northern France that serves the commune of Taverny on the Transilien network from Paris.
-
B.
Vaucelles station
Vaucelles station is a railway station serving the commune of Taverny in the northern suburbs of Paris, France.
-
C.
Raspail station
Raspail station is a Paris Métro station serving lines 4 and 6, located in the city's 14th arrondissement.
-
D.
Parc station
Parc station is a metro stop on the Charleroi Metro network in Charleroi, Belgium.
-
E.
Parc station
Parc station is a Montreal Metro station in the Parc-Extension area that serves the Blue Line and connects with commuter rail services.
- 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: Courchavon railway station Triple: [Courchavon, hasRailwayStation, Courchavon railway station]
Generated description
Courchavon railway station is a small regional train stop in the municipality of Courchavon in the Swiss canton of Jura.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Courchavon railway station Target entity description: Courchavon railway station is a small regional train stop in the municipality of Courchavon in the Swiss canton of Jura.
-
A.
Taverny station
Taverny station is a suburban railway stop in the Val-d'Oise department of northern France that serves the commune of Taverny on the Transilien network from Paris.
-
B.
Vaucelles station
Vaucelles station is a railway station serving the commune of Taverny in the northern suburbs of Paris, France.
-
C.
Raspail station
Raspail station is a Paris Métro station serving lines 4 and 6, located in the city's 14th arrondissement.
-
D.
Parc station
Parc station is a Montreal Metro station in the Parc-Extension area that serves the Blue Line and connects with commuter rail services.
-
E.
Parc station
Parc station is a metro stop on the Charleroi Metro network in Charleroi, Belgium.
- 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_69d8d38345688190b565eac2e4cd7935 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5264748dc8190984501af3e4b2036 |
completed | April 19, 2026, 7 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a040fe3a8248190ab103d8e41141113 |
completed | May 13, 2026, 5:45 a.m. |
| NEDg | Description generation | batch_6a041176a7e88190a38e3ace547212c2 |
completed | May 13, 2026, 5:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0412085b788190b4308f7ece1c7fa3 |
completed | May 13, 2026, 5:54 a.m. |
Created at: April 10, 2026, 11:30 a.m.