Triple
T2251802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sudetes |
E49632
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Wałbrzych |
E310204
|
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: Wałbrzych | Statement: [Sudetes, hasCity, Wałbrzych]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wałbrzych Context triple: [Sudetes, hasCity, Wałbrzych]
-
A.
Wałbrzych
chosen
Wałbrzych is a city in southwestern Poland known for its industrial heritage, historic coal mining, and proximity to the Sudetes mountains.
-
B.
Chorzów
Chorzów is an industrial city in southern Poland’s Silesian region, known for its heavy industry heritage and the extensive Silesian Park.
-
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.
Gliwice
Gliwice is a historic industrial and academic city in southern Poland’s Silesian region, known for its engineering university and the landmark Gliwice Radio Tower.
-
E.
Bielsko-Biała
Bielsko-Biała is a city in southern Poland at the foot of the Beskid Mountains, known as a regional industrial and cultural center formed from the historic towns of Bielsko and Biała.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc11d04688190abc04fac3a1804a9 |
completed | March 7, 2026, 6:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2f39ad7488190a7604a113c2e2bb3 |
completed | March 12, 2026, 5:10 p.m. |
Created at: March 4, 2026, 7:47 p.m.