Triple
T12420594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leverkusen |
E296756
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Villeneuve-d’Ascq |
E157888
|
NE FINISHED |
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: Villeneuve-d’Ascq | Statement: [Leverkusen, hasTwinTown, Villeneuve-d’Ascq]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Villeneuve-d’Ascq Context triple: [Leverkusen, hasTwinTown, Villeneuve-d’Ascq]
-
A.
Villeneuve d’Ascq
chosen
Villeneuve d’Ascq is a suburban city in northern France near Lille, known for its universities, technology parks, and modernist urban planning.
-
B.
Le Pecq
Le Pecq is a suburban commune in the Yvelines department of north-central France, located on the Seine River to the west of Paris.
-
C.
Lillebonne
Lillebonne is a historic town in northern France’s Normandy region, known for its Roman archaeological remains and medieval heritage.
-
D.
Boulogne-sur-Seine
Boulogne-sur-Seine was a former commune in the western suburbs of Paris, France, now part of Boulogne-Billancourt, known historically as a residential and industrial area along the Seine River.
-
E.
Valenciennes
Valenciennes is a historic industrial city in northern France near the Belgian border, known for its former coal and steel industries and its rich artistic and architectural heritage.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d6efd748190a5d9396a343e41e1 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ea44a808190af2c5a6633120814 |
completed | May 2, 2026, 8:29 p.m. |
Created at: April 8, 2026, 9:55 p.m.