Triple
T20459153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Brandenburg |
E501876
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Fläming |
—
|
NE NERFINISHED |
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: Fläming | Statement: [Mark Brandenburg, contains, Fläming]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fläming Context triple: [Mark Brandenburg, contains, Fläming]
-
A.
Fläming
chosen
Fläming is a low mountain and heathland region in eastern Germany known for its forests, rolling hills, and historic towns.
-
B.
Trendelburg
Trendelburg is a small historic town in northern Hesse, Germany, best known for its medieval castle and association with the Rapunzel fairy tale.
-
C.
Schwarzburger
A Schwarzburger was a citizen or native of the former small German state of Schwarzburg-Rudolstadt.
-
D.
Vohenstrauß
Vohenstrauß is a small town in the Upper Palatinate region of Bavaria, Germany, known for its historic architecture and surrounding forested landscapes.
-
E.
Prignitz
Prignitz is a rural district in northwestern Brandenburg, Germany, known for its historic towns, agricultural landscapes, and location along the Elbe River.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ad4940819098cf2ff6413574e5 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e696a4652c8190acf79fa2e285e436 |
completed | April 20, 2026, 9:12 p.m. |
Created at: April 16, 2026, 11:33 a.m.