Triple
T18244720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langensalza |
E436920
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Bad Langensalza |
—
|
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 Langensalza | Statement: [Langensalza, hasAlternativeName, Bad Langensalza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Langensalza Context triple: [Langensalza, hasAlternativeName, Bad Langensalza]
-
A.
Bad Langensalza
chosen
Bad Langensalza is a historic spa town in Thuringia, Germany, known for its thermal baths, well-preserved old town, and numerous themed gardens.
-
B.
Langelsheim
Langelsheim is a small town in Lower Saxony, Germany, situated in the Harz region and known for its scenic surroundings and historical mining heritage.
-
C.
Bad Klosterlausnitz
Bad Klosterlausnitz is a spa town in the German state of Thuringia, known for its therapeutic facilities and surrounding forested landscapes.
-
D.
Bad Salzungen
Bad Salzungen is a spa town in Thuringia, Germany, known for its saline springs and therapeutic health resorts.
-
E.
Treuenbrietzen
Treuenbrietzen is a historic town in the German state of Brandenburg, known for its medieval architecture and role in Reformation-era history.
- 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f7e5d63081908d0e6249578867a1 |
completed | April 19, 2026, 3:42 p.m. |
Created at: April 10, 2026, 10:33 a.m.