Triple
T4734887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Hallwil |
E105099
|
entity |
| Predicate | nearSettlement |
P3883
|
FINISHED |
| Object |
Seengen
Seengen is a Swiss municipality in the canton of Aargau, known for its scenic location in the Seetal valley and proximity to Lake Hallwil.
|
E471114
|
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: Seengen | Statement: [Lake Hallwil, nearSettlement, Seengen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seengen Context triple: [Lake Hallwil, nearSettlement, Seengen]
-
A.
Selzach
Selzach is a Swiss municipality located in the canton of Solothurn, known for its rural character and proximity to the Jura Mountains.
-
B.
Sursee
Sursee is a historic Swiss town in the canton of Lucerne, known for its well-preserved medieval old town and scenic setting near Lake Sempach.
-
C.
Neuenegg
Neuenegg is a Swiss municipality in the canton of Bern, known for its rural character and location near the city of Bern.
-
D.
Bönigen
Bönigen is a Swiss village in the canton of Bern, known for its scenic location on the shore of Lake Brienz near Interlaken.
-
E.
Göschenen
Göschenen is a Swiss mountain village and railway junction in the canton of Uri, known as a gateway to the Gotthard region.
- 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: Seengen Triple: [Lake Hallwil, nearSettlement, Seengen]
Generated description
Seengen is a Swiss municipality in the canton of Aargau, known for its scenic location in the Seetal valley and proximity to Lake Hallwil.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Seengen Target entity description: Seengen is a Swiss municipality in the canton of Aargau, known for its scenic location in the Seetal valley and proximity to Lake Hallwil.
-
A.
Selzach
Selzach is a Swiss municipality located in the canton of Solothurn, known for its rural character and proximity to the Jura Mountains.
-
B.
Sursee
Sursee is a historic Swiss town in the canton of Lucerne, known for its well-preserved medieval old town and scenic setting near Lake Sempach.
-
C.
Neuenegg
Neuenegg is a Swiss municipality in the canton of Bern, known for its rural character and location near the city of Bern.
-
D.
Bönigen
Bönigen is a Swiss village in the canton of Bern, known for its scenic location on the shore of Lake Brienz near Interlaken.
-
E.
Göschenen
Göschenen is a Swiss mountain village and railway junction in the canton of Uri, known as a gateway to the Gotthard region.
- 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_69bd43ee52048190b81a4f066534ffb3 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd648148b48190ae30631115339ba1 |
completed | March 20, 2026, 3:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be4d8230208190bfa833f12573f78f |
completed | March 21, 2026, 7:49 a.m. |
| NEDg | Description generation | batch_69be4e38bccc81909102f922fd395568 |
completed | March 21, 2026, 7:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be4ea8fa708190909e26268b49b678 |
completed | March 21, 2026, 7:54 a.m. |
Created at: March 20, 2026, 1:19 p.m.