Triple
T19391633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brenz |
E485081
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Bächingen an der Brenz |
—
|
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: Bächingen an der Brenz | Statement: [Brenz, flowsThrough, Bächingen an der Brenz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bächingen an der Brenz Context triple: [Brenz, flowsThrough, Bächingen an der Brenz]
-
A.
Bächingen an der Brenz
chosen
Bächingen an der Brenz is a small municipality in the Bavarian region of Swabia in southern Germany.
-
B.
Obergriesbach
Obergriesbach is a small municipality in the district of Aichach-Friedberg in the Bavarian region of Germany.
-
C.
Giengen an der Brenz
Giengen an der Brenz is a small town in the state of Baden-Württemberg in southern Germany, known as the birthplace of the Steiff teddy bear.
-
D.
Biebelried
Biebelried is a small municipality in the Kitzingen district of Bavaria, Germany, known for its rural character and proximity to the Franconian wine region.
-
E.
Burgkirchen
Burgkirchen is a municipality in southeastern Bavaria, Germany, known for its location in the rural, industrially influenced region near the Austrian border.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61b45caec81909dafdf66b361effd |
completed | April 20, 2026, 12:25 p.m. |
Created at: April 10, 2026, 1:36 p.m.