Triple
T10075835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lankwitz |
E213752
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Marienfelde |
E221065
|
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: Marienfelde | Statement: [Lankwitz, borderedBy, Marienfelde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marienfelde Context triple: [Lankwitz, borderedBy, Marienfelde]
-
A.
Marienfelde
chosen
Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
-
B.
Marieberg
Marieberg is a small residential and institutional district on the island of Kungsholmen in central Stockholm, Sweden.
-
C.
Nittendorf
Nittendorf is a municipality in the Upper Palatinate region of Bavaria, Germany, situated west of the city of Regensburg.
-
D.
Birkenwerder
Birkenwerder is a small municipality in the German state of Brandenburg, located just north of Berlin and known for its residential character and surrounding forests.
-
E.
Lippendorf
Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
- 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_69ca839add308190b57d53b4ec21f2d0 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd0190d808190847ea0fa401ef06c |
completed | April 2, 2026, 2:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e5755a4081909c582bf16dd285e7 |
completed | April 5, 2026, 10:43 p.m. |
Created at: March 30, 2026, 8:59 p.m.