Triple
T15815441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sihlsee |
E383463
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Einsiedeln |
E576200
|
NE FINISHED |
How this triple was built (2 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: [Sihlsee, locatedNear, Einsiedeln]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Einsiedeln Context triple: [Sihlsee, locatedNear, Einsiedeln]
-
A.
Einsiedeln
chosen
Einsiedeln is a Swiss town in the canton of Schwyz, best known for its Benedictine monastery and status as an important Catholic pilgrimage site.
-
B.
Küssnacht
Küssnacht is a picturesque Swiss municipality in the canton of Schwyz, known for its lakeside setting, historic village center, and association with the William Tell legend.
-
C.
Engelberg Abbey
Engelberg Abbey is a historic Benedictine monastery in the Swiss Alpine village of Engelberg, renowned for its centuries-old religious, cultural, and architectural heritage.
-
D.
Bremgarten
Bremgarten is a historic Swiss town in the canton of Aargau, known for its well-preserved medieval old town and scenic riverside setting.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86da2858c819090cc8481e7207b6e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0c4a219508190b8588120ec415ac7 |
completed | April 16, 2026, 11:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa131784c8190bd6aba2cca084d20 |
completed | May 9, 2026, 9:03 p.m. |
Created at: April 10, 2026, 4:49 a.m.