Triple
T2136265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brandenburg |
E46660
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Oranienburg |
E217564
|
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: Oranienburg | Statement: [Brandenburg, hasCity, Oranienburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oranienburg Context triple: [Brandenburg, hasCity, Oranienburg]
-
A.
Oranienburg
chosen
Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
-
B.
Dessau
Dessau is a German city best known for its association with the Bauhaus movement and its iconic modernist architecture.
-
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.
Fürstenwalde
Fürstenwalde is a town in eastern Germany’s Brandenburg region, known for its location on the River Spree and its historic churches and medieval architecture.
-
E.
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.
- 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_69a88a174ab48190a5db20c132e5dccf |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbdc4ce8c81908d143d5451681e6a |
completed | March 7, 2026, 5:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b333ef7f30819086b538cdc2cefe0f |
completed | March 12, 2026, 9:45 p.m. |
Created at: March 4, 2026, 7:44 p.m.