Triple
T14131918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corsier |
E350188
|
entity |
| Predicate | hasBorderWith |
P224
|
FINISHED |
| Object | Hermance |
E72184
|
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: Hermance | Statement: [Corsier, hasBorderWith, Hermance]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hermance Context triple: [Corsier, hasBorderWith, Hermance]
-
A.
Hermance
chosen
Hermance is a small lakeside municipality on the shores of Lake Geneva in southwestern Switzerland.
-
B.
Delémont
Delémont is a historic town in northwestern Switzerland that serves as the capital of the canton of Jura.
-
C.
Arlesheim
Arlesheim is a municipality in the canton of Basel-Landschaft in northwestern Switzerland, known for its historic cathedral and picturesque setting near Basel.
-
D.
Céligny
Céligny is a small, affluent Swiss village on the shores of Lake Geneva, known for its picturesque setting and as the burial place of actor Richard Burton.
-
E.
Saignelégier
Saignelégier is a municipality in the Swiss canton of Jura known for its rural landscapes, watchmaking heritage, and the annual Marché-Concours horse festival.
- 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de610cece88190b4a86500677e5938 |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd54fc136881908f0ff5cafa604811 |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 9, 2026, 11:16 p.m.