Triple
T9558024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Veveyse district |
E230591
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Bossonnens
Bossonnens is a small municipality in the canton of Fribourg in western Switzerland.
|
E806446
|
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: Bossonnens | Statement: [Veveyse district, contains, Bossonnens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bossonnens Context triple: [Veveyse district, contains, Bossonnens]
-
A.
Bönigen
Bönigen is a Swiss village in the canton of Bern, known for its scenic location on the shore of Lake Brienz near Interlaken.
-
B.
Bardonnex
Bardonnex is a small Swiss municipality located in the canton of Geneva, near the country’s border with France.
-
C.
Saint-Prex
Saint-Prex is a picturesque medieval town on the shores of Lake Geneva in the canton of Vaud, Switzerland, known for its historic old town and lakeside setting.
-
D.
Walchwil
Walchwil is a picturesque Swiss municipality in the canton of Zug, known for its scenic location on the eastern shore of Lake Zug and views of the surrounding Alps.
-
E.
Morges
Morges is a Swiss town on the shores of Lake Geneva that historically hosted the founding of the World Wildlife Fund and serves as a local cultural and economic center in the canton of Vaud.
- 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: Bossonnens Triple: [Veveyse district, contains, Bossonnens]
Generated description
Bossonnens is a small municipality in the canton of Fribourg in western Switzerland.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bossonnens Target entity description: Bossonnens is a small municipality in the canton of Fribourg in western Switzerland.
-
A.
Bönigen
Bönigen is a Swiss village in the canton of Bern, known for its scenic location on the shore of Lake Brienz near Interlaken.
-
B.
Bardonnex
Bardonnex is a small Swiss municipality located in the canton of Geneva, near the country’s border with France.
-
C.
Saint-Prex
Saint-Prex is a picturesque medieval town on the shores of Lake Geneva in the canton of Vaud, Switzerland, known for its historic old town and lakeside setting.
-
D.
Walchwil
Walchwil is a picturesque Swiss municipality in the canton of Zug, known for its scenic location on the eastern shore of Lake Zug and views of the surrounding Alps.
-
E.
Morges
Morges is a Swiss town on the shores of Lake Geneva that historically hosted the founding of the World Wildlife Fund and serves as a local cultural and economic center in the canton of Vaud.
- 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_69ca847e53a88190a60eed7e02257f10 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99487b4c819086e02e29e37b593f |
completed | April 1, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d15296303c8190adda4b24036d9390 |
completed | April 4, 2026, 6:04 p.m. |
| NEDg | Description generation | batch_69d1539bad8481909f9bd060aa3b651c |
completed | April 4, 2026, 6:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d154567f408190a848eea4ca905fb6 |
completed | April 4, 2026, 6:11 p.m. |
Created at: March 30, 2026, 8:03 p.m.