Triple
T9495406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trave |
E228990
|
entity |
| Predicate | hasConfluenceWith |
P2416
|
FINISHED |
| Object | Beste near Bad Oldesloe |
E491628
|
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: Beste near Bad Oldesloe | Statement: [Trave, hasConfluenceWith, Beste near Bad Oldesloe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beste near Bad Oldesloe Context triple: [Trave, hasConfluenceWith, Beste near Bad Oldesloe]
-
A.
Bad Segeberg
Bad Segeberg is a small spa town in northern Germany best known for its limestone caves and annual Karl May Festival.
-
B.
Bad Oldesloe
chosen
Bad Oldesloe is a small town in northern Germany’s Schleswig-Holstein state, known for its historic market center and location between Hamburg and Lübeck.
-
C.
Groß Borstel
Groß Borstel is a residential district of Hamburg, Germany, situated near Hamburg Airport and characterized by a mix of urban housing and green spaces.
-
D.
Bandorf
Bandorf is a small district of the town of Remagen in the Rhineland-Palatinate region of western Germany.
-
E.
St. Peter-Ording
St. Peter-Ording is a popular seaside resort town on Germany’s North Sea coast, known for its expansive sandy beaches, stilt houses, and spa tourism.
- 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd95eb87b081908fc7255598cd9a24 |
completed | April 1, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12d34967881909980be6f1be80885 |
completed | April 4, 2026, 3:24 p.m. |
Created at: March 30, 2026, 7:56 p.m.