Triple
T3688035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mulde |
E78272
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Bitterfeld-Wolfen |
E118183
|
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: Bitterfeld-Wolfen | Statement: [Mulde, flowsThrough, Bitterfeld-Wolfen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bitterfeld-Wolfen Context triple: [Mulde, flowsThrough, Bitterfeld-Wolfen]
-
A.
Bitterfeld-Wolfen
chosen
Bitterfeld-Wolfen is a town in Saxony-Anhalt, Germany, known for its industrial heritage, particularly in chemical production and film manufacturing.
-
B.
Weiterstadt
Weiterstadt is a town in the German state of Hesse, located near Darmstadt and known for its residential areas and commercial centers.
-
C.
Weißenfels
Weißenfels is a historic town in the German state of Saxony-Anhalt, known for its baroque architecture and former prominence as a ducal residence and industrial center.
-
D.
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.
-
E.
Oranienburg
Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
- 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_69ad85e285a081908f8cbfa9e2ed9b75 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc4c960788190b73ede08658846aa |
completed | March 8, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b54c256e0881909c6d481d373e814c |
completed | March 14, 2026, 11:53 a.m. |
Created at: March 8, 2026, 3:26 p.m.