Triple
T12691175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sudetes |
E303205
|
entity |
| Predicate | hasCityNearby |
P3883
|
FINISHED |
| Object | Kłodzko |
E217920
|
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: Kłodzko | Statement: [Sudetes, hasCityNearby, Kłodzko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kłodzko Context triple: [Sudetes, hasCityNearby, Kłodzko]
-
A.
Kłodzko
chosen
Kłodzko is a historic town in southwestern Poland known for its well-preserved medieval architecture and prominent hilltop fortress.
-
B.
Świdnica
Świdnica is a historic town in southwestern Poland known for its well-preserved medieval architecture and the UNESCO-listed Church of Peace.
-
C.
Cieszyn
Cieszyn is a historic town in southern Poland on the Olza River, known for its shared Polish-Czech heritage and well-preserved old town.
-
D.
Krosno
Krosno is a historic town in southeastern Poland known for its glassmaking industry and well-preserved old town.
-
E.
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.
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961dabb38819087738361f9de8066 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c7a79908190b83a868090990bbe |
completed | May 2, 2026, 10:36 p.m. |
Created at: April 9, 2026, 5:22 p.m.