Triple
T6206542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lons-le-Saunier |
E138761
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object |
Einsiedeln
Einsiedeln is a Swiss town in the canton of Schwyz, best known for its Benedictine monastery and status as an important Catholic pilgrimage site.
|
E576200
|
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: Einsiedeln | Statement: [Lons-le-Saunier, twinTown, Einsiedeln]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Einsiedeln Context triple: [Lons-le-Saunier, twinTown, Einsiedeln]
-
A.
Grenchen
Grenchen is a Swiss town in the canton of Solothurn known for its watchmaking industry and location at the foot of the Jura Mountains.
-
B.
Ramiswil
Ramiswil is a small rural municipality in the canton of Solothurn in northwestern Switzerland, known for its scenic Jura landscape and agricultural character.
-
C.
Illnau-Effretikon
Illnau-Effretikon is a municipality in the canton of Zürich in Switzerland, known for its mix of suburban residential areas and rural landscapes.
-
D.
Landquart
Landquart is a river in eastern Switzerland that flows through the canton of Graubünden before joining the Alpine Rhine.
-
E.
Romanshorn
Romanshorn is a Swiss town on the southern shore of Lake Constance, known as an important regional transport hub and ferry port.
- 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: Einsiedeln Triple: [Lons-le-Saunier, twinTown, Einsiedeln]
Generated description
Einsiedeln is a Swiss town in the canton of Schwyz, best known for its Benedictine monastery and status as an important Catholic pilgrimage site.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Einsiedeln Target entity description: Einsiedeln is a Swiss town in the canton of Schwyz, best known for its Benedictine monastery and status as an important Catholic pilgrimage site.
-
A.
Grenchen
Grenchen is a Swiss town in the canton of Solothurn known for its watchmaking industry and location at the foot of the Jura Mountains.
-
B.
Ramiswil
Ramiswil is a small rural municipality in the canton of Solothurn in northwestern Switzerland, known for its scenic Jura landscape and agricultural character.
-
C.
Illnau-Effretikon
Illnau-Effretikon is a municipality in the canton of Zürich in Switzerland, known for its mix of suburban residential areas and rural landscapes.
-
D.
Landquart
Landquart is a river in eastern Switzerland that flows through the canton of Graubünden before joining the Alpine Rhine.
-
E.
Romanshorn
Romanshorn is a Swiss town on the southern shore of Lake Constance, known as an important regional transport hub and ferry port.
- 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_69c008acbea48190991c6b834bb45d65 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0626f85748190a94448117a85fd78 |
completed | March 22, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16f47b04c81909bfe20305911f5f2 |
completed | March 23, 2026, 4:50 p.m. |
| NEDg | Description generation | batch_69c1f5a1374481909117f2d8582ef195 |
completed | March 24, 2026, 2:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1f60573a481909440f2ba7ef01f79 |
completed | March 24, 2026, 2:25 a.m. |
Created at: March 22, 2026, 4:20 p.m.