Triple
T374357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Geneva |
E8337
|
entity |
| Predicate | bordersCity |
P224
|
FINISHED |
| Object |
Évian-les-Bains
Évian-les-Bains is a French spa and resort town in the Alps renowned worldwide for its mineral water and scenic lakeside setting.
|
E78937
|
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: Évian-les-Bains | Statement: [Lake Geneva, bordersCity, Évian-les-Bains]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Évian-les-Bains Context triple: [Lake Geneva, bordersCity, Évian-les-Bains]
-
A.
Mougins
Mougins is a picturesque hilltop village in southeastern France, renowned for its art scene, gastronomy, and association with many famous artists.
-
B.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
C.
Mulhouse
Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
-
D.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
-
E.
Vichy
Vichy is a spa town in central France renowned for its thermal springs, health resorts, and role as the seat of the World War II Vichy regime.
- 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: Évian-les-Bains Triple: [Lake Geneva, bordersCity, Évian-les-Bains]
Generated description
Évian-les-Bains is a French spa and resort town in the Alps renowned worldwide for its mineral water and scenic lakeside setting.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Évian-les-Bains Target entity description: Évian-les-Bains is a French spa and resort town in the Alps renowned worldwide for its mineral water and scenic lakeside setting.
-
A.
Mougins
Mougins is a picturesque hilltop village in southeastern France, renowned for its art scene, gastronomy, and association with many famous artists.
-
B.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
C.
Mulhouse
Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
-
D.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
-
E.
Vichy
Vichy is a spa town in central France renowned for its thermal springs, health resorts, and role as the seat of the World War II Vichy regime.
- 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_69a2e7f2ec648190b42bc7db424f8109 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ee2b0ec481908fac41a4e1d20468 |
completed | Feb. 28, 2026, 1:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5692e9f248190945be16aac260038 |
completed | March 2, 2026, 10:40 a.m. |
| NEDg | Description generation | batch_69a56ae958f481909b097848ef1d9b5d |
completed | March 2, 2026, 10:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a56b86d684819080c398847af0551b |
completed | March 2, 2026, 10:50 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.