Triple
T17591165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucerne region |
E428450
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Kriens |
—
|
NE NERFINISHED |
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: Kriens | Statement: [Lucerne region, contains, Kriens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kriens Context triple: [Lucerne region, contains, Kriens]
-
A.
Kriens
chosen
Kriens is a Swiss town and municipality located near Lucerne, known as a residential and industrial suburb at the foot of Mount Pilatus.
-
B.
Sursee
Sursee is a historic Swiss town in the canton of Lucerne, known for its well-preserved medieval old town and scenic setting near Lake Sempach.
-
C.
Kilchberg
Kilchberg is a municipality on the shores of Lake Zurich in Switzerland, known for its scenic residential character and as the home of the Lindt & Sprüngli chocolate factory.
-
D.
Oberkorn
Oberkorn is a town in southwestern Luxembourg, known as a residential locality within the industrial and cross-border region near Differdange and the Belgian and French borders.
-
E.
Bremgarten
Bremgarten is a historic Swiss town in the canton of Aargau, known for its well-preserved medieval old town and scenic riverside setting.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469e6e3888190b73a5b6d7e8c0a55 |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 5:51 a.m.