Triple
T21996273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bezirk Cottbus |
E543213
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Hoyerswerda |
—
|
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: Hoyerswerda | Statement: [Bezirk Cottbus, contains, Hoyerswerda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hoyerswerda Context triple: [Bezirk Cottbus, contains, Hoyerswerda]
-
A.
Hoyerswerda
chosen
Hoyerswerda is a town in eastern Germany’s Saxony region, historically shaped by lignite mining and now known for its proximity to the emerging Lusatian lake landscape.
-
B.
Bautzen
Bautzen is a historic town in eastern Germany known for its well-preserved medieval architecture and as a cultural center of the Sorbian minority.
-
C.
Görlitz
Görlitz is a historic city in eastern Germany on the Lusatian Neisse River, known for its well-preserved old town and role as a popular film location.
-
D.
Zschopau
Zschopau is a historic town in Saxony, Germany, known for its location in the Ore Mountains and its long tradition of motorcycle manufacturing.
-
E.
Bischofswerda
Bischofswerda is a small town in the Saxony region of eastern Germany, known as a local commercial and transport hub near the city of Dresden.
- 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12765fb0c81908f7b7acda065ee2f |
completed | April 28, 2026, 9:32 p.m. |
Created at: April 16, 2026, 8:19 p.m.