Triple
T11439598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aue-Bad Schlema |
E271106
|
entity |
| Predicate | formedByMergerOf |
P77
|
FINISHED |
| Object | Bad Schlema |
E926655
|
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: Bad Schlema | Statement: [Aue-Bad Schlema, formedByMergerOf, Bad Schlema]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Schlema Context triple: [Aue-Bad Schlema, formedByMergerOf, Bad Schlema]
-
A.
Bad Schlema
chosen
Bad Schlema is a spa town in Saxony, Germany, known for its radon and mineral springs and its history of uranium mining.
-
B.
Bad Brambach
Bad Brambach is a German spa town in the Vogtland region of Saxony, renowned for its mineral springs and therapeutic health resorts.
-
C.
Bad Bayersoien
Bad Bayersoien is a small spa village and municipality in Bavaria, Germany, known for its scenic lakeside setting and traditional Upper Bavarian character.
-
D.
Bad Tennstedt
Bad Tennstedt is a small spa town in Thuringia, Germany, known for its mineral springs and location in the Unstrut river landscape.
-
E.
Bad Nauheim
Bad Nauheim is a spa town in the German state of Hesse, historically known for its therapeutic mineral springs and health resorts.
- 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d80888190c8190b6365550ffe4931c |
completed | April 9, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5e8ed579c8190b1ddc1dce20d9617 |
completed | April 20, 2026, 8:50 a.m. |
Created at: April 8, 2026, 9:35 p.m.