Triple
T8052986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Lauchstädt |
E187719
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Bad Lauchstädt |
E187719
|
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 Lauchstädt | Statement: [Bad Lauchstädt, hasName, Bad Lauchstädt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Lauchstädt Context triple: [Bad Lauchstädt, hasName, Bad Lauchstädt]
-
A.
Bad Lauchstädt
chosen
Bad Lauchstädt is a historic spa town in the German state of Saxony-Anhalt, known for its classical Kurpark and Goethe-Theater.
-
B.
Bad Rothenfelde
Bad Rothenfelde is a spa town in Lower Saxony, Germany, known for its saline springs and health resort facilities.
-
C.
Bad Säckingen
Bad Säckingen is a historic spa town in southwestern Germany on the Rhine River, known for its medieval old town and one of the longest covered wooden bridges in Europe.
-
D.
Bad Salzuflen
Bad Salzuflen is a German spa town in the Lippe district of North Rhine-Westphalia, known for its saltwater springs and historic half-timbered architecture.
-
E.
Bad Tölz
Bad Tölz is a Bavarian spa town in southern Germany known for its historic old town, alpine scenery, and traditional German architecture.
- 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_69ca82b15e948190a62fd7af5218426a |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f7c425c8190aa1b2f534afeb58c |
completed | March 31, 2026, 3:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc572058048190996fa77774bf44ba |
completed | March 31, 2026, 11:22 p.m. |
Created at: March 30, 2026, 5:25 p.m.