Triple
T7323595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lutherstadt Wittenberg station |
E168811
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Eilenburg |
E444915
|
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: Eilenburg | Statement: [Lutherstadt Wittenberg station, connectsTo, Eilenburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eilenburg Context triple: [Lutherstadt Wittenberg station, connectsTo, Eilenburg]
-
A.
Eilenburg
chosen
Eilenburg is a small historic town in the German state of Saxony, situated on the Mulde River northeast of Leipzig.
-
B.
Oranienburg
Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
-
C.
Wurzen
Wurzen is a historic town in the German state of Saxony, known for its medieval architecture and location on the river Mulde east of Leipzig.
-
D.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
-
E.
Ilmenau
Ilmenau is a German town best known for its location in the Thuringian Forest and its association with the poet Johann Wolfgang von Goethe.
- 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_69c68a54cacc81908e3b773441f19566 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f046b93c8190a80dd48ee409ec5d |
completed | March 27, 2026, 9:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c86829f0788190828f5fb659e311d0 |
completed | March 28, 2026, 11:45 p.m. |
Created at: March 27, 2026, 3:03 p.m.