Triple
T15862972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monte Rosa glaciers |
E384636
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Signalkuppe |
E360788
|
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: Signalkuppe | Statement: [Monte Rosa glaciers, near, Signalkuppe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Signalkuppe Context triple: [Monte Rosa glaciers, near, Signalkuppe]
-
A.
Signalkuppe
chosen
Signalkuppe is a prominent peak in the Pennine Alps on the border between Italy and Switzerland, known for hosting the high-altitude Capanna Regina Margherita hut near its summit.
-
B.
Bärenkopf
Bärenkopf is a mountain peak in the Austrian Alps that forms part of the Glockner Group.
-
C.
Schneidhain
Schneidhain is a district of the town Königstein im Taunus in the Hochtaunus region of Hesse, Germany.
-
D.
Brocken
Brocken is a prominent mountain in central Germany’s Harz range, known for its harsh climate, folklore, and role in literature and cultural history.
-
E.
Teufelstättkopf
Teufelstättkopf is a mountain peak in the Bavarian Ammergau Alps, popular with hikers for its scenic views and relatively accessible trails.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1555d38fc8190bd8820bb5b238b71 |
completed | April 16, 2026, 9:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa9439db481908be2f6d8a3cfbc85 |
completed | May 9, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:50 a.m.