Triple
T5625091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lagginhorn |
E147698
|
entity |
| Predicate | nearestTown |
P350
|
FINISHED |
| Object | Saas-Grund |
E429927
|
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: Saas-Grund | Statement: [Lagginhorn, nearestTown, Saas-Grund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saas-Grund Context triple: [Lagginhorn, nearestTown, Saas-Grund]
-
A.
Saas-Grund
chosen
Saas-Grund is a Swiss alpine village and municipality in the canton of Valais, known as a gateway to the Saas Valley ski and hiking region.
-
B.
Saas-Balen
Saas-Balen is a small Swiss mountain village in the canton of Valais, known for its alpine scenery and proximity to the popular resort of Saas-Fee.
-
C.
Grund
Grund is a historic, picturesque quarter of Luxembourg City known for its riverside setting, old architecture, and vibrant nightlife.
-
D.
Hausberg
Hausberg is a popular ski mountain and recreational area near Garmisch-Partenkirchen in the Bavarian Alps.
-
E.
Saalhof
Saalhof is a historic medieval building complex in Frankfurt am Main that forms part of the city’s museum landscape and reflects its architectural and urban history.
- 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_69c00906f2a88190a992c66b13d606d4 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c02235b4e48190a529f70605bf47ca |
completed | March 22, 2026, 5:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c02882babc819093c987c745615865 |
completed | March 22, 2026, 5:36 p.m. |
Created at: March 22, 2026, 3:40 p.m.