Triple
T4286723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taunus |
E97286
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Bad Soden am Taunus |
E221174
|
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 Soden am Taunus | Statement: [Taunus, containsTown, Bad Soden am Taunus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Soden am Taunus Context triple: [Taunus, containsTown, Bad Soden am Taunus]
-
A.
Bad Soden am Taunus
chosen
Bad Soden am Taunus is a spa town in Hesse, Germany, known for its mineral springs and location on the slopes of the Taunus mountains near Frankfurt.
-
B.
Hofheim am Taunus
Hofheim am Taunus is a town in the German state of Hesse, located near Frankfurt within the Taunus mountain region.
-
C.
Bad Schwalbach
Bad Schwalbach is a spa town in the German state of Hesse, known for its mineral springs and location in the Taunus mountains.
-
D.
Badenweiler
Badenweiler is a spa town in southwestern Germany’s Black Forest region, known for its thermal baths and as the place where Russian writer Anton Chekhov died.
-
E.
Uerdingen
Uerdingen is a district of the German city of Krefeld, known historically for its chemical industry and location along the Rhine River.
- 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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3505d23d88190a638f2cc2acee9ee |
completed | March 12, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d06ccbf081908bc23b5f194b9587 |
completed | March 14, 2026, 9:17 p.m. |
Created at: March 12, 2026, 11:08 p.m.