Triple
T10461394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Śnieżka |
E246681
|
entity |
| Predicate | isAccessibleFrom |
P1985
|
FINISHED |
| Object | Karpacz |
E246686
|
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: Karpacz | Statement: [Śnieżka, isAccessibleFrom, Karpacz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karpacz Context triple: [Śnieżka, isAccessibleFrom, Karpacz]
-
A.
Karpacz
chosen
Karpacz is a popular mountain resort town in southwestern Poland, known for skiing, hiking, and its location at the foot of Śnieżka in the Sudetes.
-
B.
Zakopane
Zakopane is a popular resort town in southern Poland, known as the country's "winter capital" and a gateway to the Tatra Mountains.
-
C.
Szczyrk
Szczyrk is a popular mountain resort town in southern Poland, known for its ski slopes, hiking trails, and scenic location in the Silesian Beskids.
-
D.
Jelenia Góra
Jelenia Góra is a historic city in southwestern Poland, known for its picturesque setting in the Karkonosze Mountains and its well-preserved old town architecture.
-
E.
Szklarska Poręba
Szklarska Poręba is a popular Polish mountain resort town known for skiing, hiking, and scenic landscapes in the Karkonosze range near the Czech border.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50883b62c819082711b8c9fd968e3 |
completed | April 7, 2026, 1:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89fcc84b48190a39de0d9b9111ebd |
completed | April 10, 2026, 6:59 a.m. |
Created at: April 6, 2026, 12:19 p.m.