Triple
T3688914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bode |
E78295
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Oschersleben |
E116349
|
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: Oschersleben | Statement: [Bode, flowsThrough, Oschersleben]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oschersleben Context triple: [Bode, flowsThrough, Oschersleben]
-
A.
Aschersleben
chosen
Aschersleben is a historic town in the German state of Saxony-Anhalt, known as one of the oldest documented cities in central Germany.
-
B.
Oranienburg
Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
-
C.
Neustrelitz
Neustrelitz is a town in northeastern Germany known for hosting a key research center of the German Aerospace Center (DLR), particularly focused on satellite data and space-related technologies.
-
D.
Bitterfeld-Wolfen
Bitterfeld-Wolfen is a town in Saxony-Anhalt, Germany, known for its industrial heritage, particularly in chemical production and film manufacturing.
-
E.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
- 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_69b5560a42688190b30bce9b7af10db4 |
completed | March 14, 2026, 12:35 p.m. |
Created at: March 8, 2026, 3:26 p.m.