Triple
T19846425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limburg-Weilburg |
E476870
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Bad Camberg |
—
|
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 Camberg | Statement: [Limburg-Weilburg, contains, Bad Camberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Camberg Context triple: [Limburg-Weilburg, contains, Bad Camberg]
-
A.
Bad Camberg
chosen
Bad Camberg is a German spa town in the state of Hesse, known for its historic half-timbered old town and therapeutic health resorts.
-
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 Breisig
Bad Breisig is a small spa town on the Rhine River in western Germany, known for its thermal baths and scenic riverside setting.
-
D.
Bad Grönenbach
Bad Grönenbach is a spa town in the Bavarian Allgäu region of southern Germany, known for its health resorts and picturesque rural surroundings.
-
E.
Bad Saarow
Bad Saarow is a German spa town in Brandenburg known for its thermal baths and lakeside setting on the Scharmützelsee.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65809da2c8190bb579ef42513b74d |
completed | April 20, 2026, 4:44 p.m. |
Created at: April 10, 2026, 1:51 p.m.