Triple
T10257388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neisse River |
E240507
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Brandenburg |
E46660
|
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: Brandenburg | Statement: [Neisse River, flowsThrough, Brandenburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brandenburg Context triple: [Neisse River, flowsThrough, Brandenburg]
-
A.
Brandenburg
chosen
Brandenburg is a federal state in northeastern Germany that surrounds Berlin and is known for its lakes, forests, and historic Prussian heritage.
-
B.
Brandenburg
Brandenburg is a small city in Meade County, Kentucky, situated along the Ohio River and serving as the county seat.
-
C.
Mecklenburg-Vorpommern
Mecklenburg-Vorpommern is a federal state in northeastern Germany known for its Baltic Sea coastline, numerous lakes, and relatively low population density.
-
D.
Saxony
Saxony is a historic region and former kingdom in eastern Germany, known for its cultural centers like Dresden and Leipzig and its significant role in Central European history.
-
E.
Weimarer Land
Weimarer Land is a rural district in the German state of Thuringia, known for its historic towns, cultural heritage, and agricultural landscapes surrounding the city of Weimar.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d24de4588190b68fb3daa36dbd7d |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71c559910819092b0eae9c05aa7dc |
completed | April 9, 2026, 3:26 a.m. |
Created at: April 6, 2026, 11:31 a.m.