Triple
T2820439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chemnitz–Leipzig railway |
E54793
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Bad Lausick |
E185206
|
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 Lausick | Statement: [Chemnitz–Leipzig railway, passesThrough, Bad Lausick]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Lausick Context triple: [Chemnitz–Leipzig railway, passesThrough, Bad Lausick]
-
A.
Bad Lausick
chosen
Bad Lausick is a small spa town in the Free State of Saxony in eastern Germany, known for its therapeutic mineral springs and health resorts.
-
B.
Bad Camberg
Bad Camberg is a German spa town in the state of Hesse, known for its historic half-timbered old town and therapeutic health resorts.
-
C.
Bad Mergentheim
Bad Mergentheim is a historic spa town in the German state of Baden-Württemberg, renowned for its mineral springs and picturesque setting in the Tauber Valley.
-
D.
Bad Harzburg
Bad Harzburg is a German spa and resort town on the northern edge of the Harz Mountains, known for its thermal baths, hiking trails, and historic castle ruins.
-
E.
Bad Ragaz
Bad Ragaz is a Swiss spa and resort town in the canton of St. Gallen, renowned for its thermal baths and alpine setting.
- 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde6e85008190a08eb2bf8e393e7e |
completed | March 7, 2026, 8:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afcea809e48190b22f25a3c8c1acdd |
completed | March 10, 2026, 7:56 a.m. |
Created at: March 6, 2026, 9:59 p.m.