Triple
T10540006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allgäu Alps |
E248669
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Nebelhorn |
E809407
|
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: Nebelhorn | Statement: [Allgäu Alps, contains, Nebelhorn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nebelhorn Context triple: [Allgäu Alps, contains, Nebelhorn]
-
A.
Nebelhorn
chosen
Nebelhorn is a prominent mountain in the Allgäu Alps of southern Germany, known for its panoramic views and popular hiking and skiing opportunities.
-
B.
Oberhof
Oberhof is a German winter sports town in Thuringia renowned for its biathlon, luge, and cross-country skiing facilities and World Cup events.
-
C.
Ясная Поляна
Ясная Поляна is a historic estate in Russia best known as the home and literary workplace of writer Leo Tolstoy, where he created major works such as "War and Peace" and "Anna Karenina."
-
D.
Schladming
Schladming is a renowned Austrian alpine ski resort town in Styria, famous for hosting major international ski races and World Cup events.
-
E.
Penzberg
Penzberg is a Bavarian town in southern Germany known for its historical coal mining industry and its modern role as a center for biotechnology and pharmaceuticals.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50a582be48190856c6f272eea4dcf |
completed | April 7, 2026, 1:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9341d96c08190a6ba644b9acfe2c8 |
completed | April 10, 2026, 5:32 p.m. |
Created at: April 6, 2026, 12:32 p.m.