Triple
T4490457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Sempach |
E107358
|
entity |
| Predicate | hasNearbySettlement |
P4647
|
FINISHED |
| Object |
Sempach
Sempach is a historic Swiss town in the canton of Lucerne, known for the nearby Battle of Sempach and its picturesque lakeside setting.
|
E446756
|
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: Sempach | Statement: [Lake Sempach, hasNearbySettlement, Sempach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sempach Context triple: [Lake Sempach, hasNearbySettlement, Sempach]
-
A.
Winterthurer
Winterthurer is the German term for an inhabitant or native of the Swiss city of Winterthur.
-
B.
Chambésy, Switzerland
Chambésy, Switzerland is a lakeside suburb of Geneva that hosts important international and ecumenical institutions, including the Orthodox Centre of the Ecumenical Patriarchate.
-
C.
Richterswil
Richterswil is a picturesque municipality on the shores of Lake Zurich in the canton of Zurich, Switzerland.
-
D.
Murten
Murten is a historic bilingual town in the canton of Fribourg, Switzerland, known for its well-preserved medieval old town and lakeside setting on Lake Murten.
-
E.
Saint-Imier
Saint-Imier is a Swiss town in the Jura region known for its long tradition of watchmaking and as the birthplace of several renowned Swiss watch brands.
- 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: Sempach Triple: [Lake Sempach, hasNearbySettlement, Sempach]
Generated description
Sempach is a historic Swiss town in the canton of Lucerne, known for the nearby Battle of Sempach and its picturesque lakeside setting.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sempach Target entity description: Sempach is a historic Swiss town in the canton of Lucerne, known for the nearby Battle of Sempach and its picturesque lakeside setting.
-
A.
Giswil
Giswil is a Swiss municipality in the canton of Obwalden, known for its scenic alpine landscape and location along key routes through central Switzerland.
-
B.
Winterthurer
Winterthurer is the German term for an inhabitant or native of the Swiss city of Winterthur.
-
C.
Chambésy, Switzerland
Chambésy, Switzerland is a lakeside suburb of Geneva that hosts important international and ecumenical institutions, including the Orthodox Centre of the Ecumenical Patriarchate.
-
D.
Richterswil
Richterswil is a picturesque municipality on the shores of Lake Zurich in the canton of Zurich, Switzerland.
-
E.
Murten
Murten is a historic bilingual town in the canton of Fribourg, Switzerland, known for its well-preserved medieval old town and lakeside setting on Lake Murten.
- 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_69bd43f84f788190a1383579c4a595be |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd556e69f88190b9c16afc2afcdbef |
completed | March 20, 2026, 2:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd67aee5908190888efb94eee725e5 |
completed | March 20, 2026, 3:28 p.m. |
| NEDg | Description generation | batch_69bd6c62f40881909e30291317ab5f99 |
completed | March 20, 2026, 3:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bd6cf9a04881909a25ce291df748a0 |
completed | March 20, 2026, 3:51 p.m. |
Created at: March 20, 2026, 12:59 p.m.