Triple
T15779416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landquart |
E382572
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | de:Landquart |
E382572
|
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: de:Landquart | Statement: [Landquart, hasNameInLanguage, de:Landquart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: de:Landquart Context triple: [Landquart, hasNameInLanguage, de:Landquart]
-
A.
Quarten
Quarten is a Swiss municipality in the canton of St. Gallen, known for its location on the shores of Lake Walen and its proximity to the Flumserberg ski and hiking area.
-
B.
Landquart
chosen
Landquart is a river in eastern Switzerland that flows through the canton of Graubünden before joining the Alpine Rhine.
-
C.
Disentis
Disentis is a Swiss Alpine village in the canton of Graubünden known for its Benedictine monastery and access to extensive skiing and mountain sports.
-
D.
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.
-
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 (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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e053fea90081908e3fe4f91475bead |
completed | April 16, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff909f7f4481909cceb32e26af3780 |
completed | May 9, 2026, 7:53 p.m. |
Created at: April 10, 2026, 4:48 a.m.