Triple
T19489170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kitzbüheler Horn |
E487601
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Kitzbühel |
—
|
NE NERFINISHED |
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: Kitzbühel | Statement: [Kitzbüheler Horn, near, Kitzbühel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kitzbühel Context triple: [Kitzbüheler Horn, near, Kitzbühel]
-
A.
Kitzbühel
chosen
Kitzbühel is a renowned Austrian alpine town famous for its ski resorts and the annual Hahnenkamm downhill race.
-
B.
Schladming
Schladming is a renowned Austrian alpine ski resort town in Styria, famous for hosting major international ski races and World Cup events.
-
C.
Mayrhofen
Mayrhofen is a popular Austrian alpine resort town known for skiing, hiking, and access to the Zillertal Alps.
-
D.
Sölden
Sölden is a renowned Austrian ski resort town in the Ötztal Valley, famous for its extensive alpine skiing, glacier slopes, and role as a regular FIS Alpine Ski World Cup venue.
-
E.
Zell am See
Zell am See is a popular Austrian alpine town and lakeside resort known for its scenic mountain setting, skiing, and outdoor recreation.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d924388190b847cb15bb3d0aff |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6348ad4088190b530f47efca90165 |
completed | April 20, 2026, 2:13 p.m. |
Created at: April 10, 2026, 1:39 p.m.