Triple
T17793781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karlskoga Municipality |
E444234
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Brzeg Dolny |
—
|
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: Brzeg Dolny | Statement: [Karlskoga Municipality, hasTwinTown, Brzeg Dolny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brzeg Dolny Context triple: [Karlskoga Municipality, hasTwinTown, Brzeg Dolny]
-
A.
Brzeg Dolny
chosen
Brzeg Dolny is a small industrial town in southwestern Poland, located on the Oder River in the Lower Silesian Voivodeship.
-
B.
Brzegi
Brzegi is a village in southern Poland located in the Tatra region, known for its mountainous surroundings and traditional highland culture.
-
C.
Byczyna
Byczyna is a historic small town in southwestern Poland known for its well-preserved medieval urban layout and defensive walls.
-
D.
Brzeg
Brzeg is a historic town in southwestern Poland known for its Renaissance castle and well-preserved old town.
-
E.
Skawina
Skawina is a town in southern Poland near Kraków, known for its industrial facilities and role as a local economic and transport hub.
- 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_69d8b9efe370819095cd219b143ae727 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e487993a6c8190805e06d93dfc0dce |
completed | April 19, 2026, 7:43 a.m. |
Created at: April 10, 2026, 10:13 a.m.