Triple
T10934282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taverny |
E258288
|
entity |
| Predicate | hasRailwayStation |
P918
|
FINISHED |
| Object |
Vaucelles station
Vaucelles station is a railway station serving the commune of Taverny in the northern suburbs of Paris, France.
|
E896238
|
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: Vaucelles station | Statement: [Taverny, hasRailwayStation, Vaucelles station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vaucelles station Context triple: [Taverny, hasRailwayStation, Vaucelles station]
-
A.
Vaneau station
Vaneau station is a Paris Métro stop in the 7th and 6th arrondissements, serving the residential and institutional Left Bank area near Invalides and several ministries.
-
B.
Raspail station
Raspail station is a Paris Métro station serving lines 4 and 6, located in the city's 14th arrondissement.
-
C.
Malesherbes station
Malesherbes station is a railway station in France serving the town of Malesherbes and connecting it to the regional rail network.
-
D.
Malesherbes station
Malesherbes station is a Paris Métro station serving the 8th and 17th arrondissements of Paris on Line 3.
-
E.
Beaurepaire station
Beaurepaire station is a commuter rail station serving the Beaurepaire area of Beaconsfield, a suburb on the Island of Montreal in Quebec, Canada.
- 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: Vaucelles station Triple: [Taverny, hasRailwayStation, Vaucelles station]
Generated description
Vaucelles station is a railway station serving the commune of Taverny in the northern suburbs of Paris, France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vaucelles station Target entity description: Vaucelles station is a railway station serving the commune of Taverny in the northern suburbs of Paris, France.
-
A.
Vaneau station
Vaneau station is a Paris Métro stop in the 7th and 6th arrondissements, serving the residential and institutional Left Bank area near Invalides and several ministries.
-
B.
Raspail station
Raspail station is a Paris Métro station serving lines 4 and 6, located in the city's 14th arrondissement.
-
C.
Malesherbes station
Malesherbes station is a Paris Métro station serving the 8th and 17th arrondissements of Paris on Line 3.
-
D.
Malesherbes station
Malesherbes station is a railway station in France serving the town of Malesherbes and connecting it to the regional rail network.
-
E.
Beaurepaire station
Beaurepaire station is a commuter rail station serving the Beaurepaire area of Beaconsfield, a suburb on the Island of Montreal in Quebec, Canada.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770ae073881909720febe9f5f296a |
completed | April 9, 2026, 9:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2d710f65c8190a4ef17a6d90a19d2 |
completed | April 18, 2026, 12:57 a.m. |
| NEDg | Description generation | batch_69e2fab58f588190ae2d33f32e71333b |
completed | April 18, 2026, 3:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e317809c0881909e793db965194014 |
completed | April 18, 2026, 5:32 a.m. |
Created at: April 8, 2026, 9:23 p.m.