Triple
T19312944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ilm |
E483016
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Bad Berka |
—
|
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: Bad Berka | Statement: [Ilm, flowsThrough, Bad Berka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Berka Context triple: [Ilm, flowsThrough, Bad Berka]
-
A.
Bad Berka
chosen
Bad Berka is a small spa town in the German state of Thuringia, known for its health resorts and scenic surroundings.
-
B.
Bad Schussenried
Bad Schussenried is a spa town in southern Germany known for its historic monastery complex and scenic location in Upper Swabia.
-
C.
Rugendorf
Rugendorf is a small municipality in the Bavarian region of Upper Franconia in Germany.
-
D.
Seckbach
Seckbach is a district in the east of Frankfurt am Main, Germany, known for its residential character and proximity to green spaces like the Lohrberg.
-
E.
Bürchen
Bürchen is a small alpine municipality and popular holiday resort in the canton of Valais in southwestern Switzerland.
- 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_69d8e8d04d5c8190baa816986f2b1d1e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e604cf25c081908a30814b15d78c25 |
completed | April 20, 2026, 10:49 a.m. |
Created at: April 10, 2026, 1:32 p.m.