Triple
T19969504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wörgl |
E480031
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Innsbruck |
—
|
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: Innsbruck | Statement: [Wörgl, locatedNear, Innsbruck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Innsbruck Context triple: [Wörgl, locatedNear, Innsbruck]
-
A.
Innsbruck
chosen
Innsbruck is a city in western Austria known for its Alpine setting and winter sports facilities, and it later successfully hosted the Winter Olympics in 1964 and 1976.
-
B.
Salzburg
Salzburg is a historic Austrian city on the Salzach River, renowned for its baroque architecture, Alpine setting, and as the birthplace of composer Wolfgang Amadeus Mozart.
-
C.
Lenzburg
Lenzburg is a historic Swiss town in the canton of Aargau, known for its medieval hilltop castle and well-preserved old town.
-
D.
Bludenz
Bludenz is a small alpine town in western Austria known as a regional hub for skiing, hiking, and chocolate production.
-
E.
Kufstein
Kufstein is a historic town in the Austrian state of Tyrol, known for its medieval fortress and picturesque setting in the Alps near the German border.
- 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc7c2fc81909b89c549a5f99e4c |
completed | April 20, 2026, 5 p.m. |
Created at: April 10, 2026, 1:54 p.m.