Triple
T15779422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landquart |
E382572
|
entity |
| Predicate | flowsThroughMunicipality |
P42402
|
FINISHED |
| Object | Grüsch |
E701547
|
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: Grüsch | Statement: [Landquart, flowsThroughMunicipality, Grüsch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grüsch Context triple: [Landquart, flowsThroughMunicipality, Grüsch]
-
A.
Grüsch
chosen
Grüsch is a Swiss municipality in the canton of Graubünden, situated in the alpine Prättigau valley and known as a gateway to nearby mountain and ski areas.
-
B.
Bramsche
Bramsche is a town in Lower Saxony, Germany, known for its location near Osnabrück and its historical textile industry.
-
C.
Ophasselt
Ophasselt is a village in the Flemish province of East Flanders, Belgium, known as one of the constituent communities of the city of Geraardsbergen.
-
D.
Wimbachgries
Wimbachgries is a broad high-alpine gravel valley and hiking area in the Bavarian Alps, known for its impressive scree fields and dramatic mountain scenery.
-
E.
Graslei
Graslei is a historic quay along the Leie River in the center of Ghent, Belgium, renowned for its picturesque row of medieval guild houses and vibrant café culture.
- 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_69e142df48e8819083a48d3b7b3f7f5d |
completed | April 16, 2026, 8:13 p.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.