Triple
T3381697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin Tegel Airport |
E71199
|
entity |
| Predicate | servedCity |
P3936
|
FINISHED |
| Object | Bernau bei Berlin |
E311039
|
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: Bernau bei Berlin | Statement: [Berlin Tegel Airport, servedCity, Bernau bei Berlin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bernau bei Berlin Context triple: [Berlin Tegel Airport, servedCity, Bernau bei Berlin]
-
A.
Bernau bei Berlin
chosen
Bernau bei Berlin is a historic town in the German state of Brandenburg, located just northeast of Berlin and known for its well-preserved medieval city walls.
-
B.
Degendorf
Degendorf is a locality within the Bavarian town and district of Lichtenfels in Germany.
-
C.
Grevesmühlen
Grevesmühlen is a small town in the German state of Mecklenburg-Vorpommern, known as a local administrative and service center in the north of the country.
-
D.
Schorfheide
Schorfheide is a large forested and lake-rich area in Brandenburg, Germany, known for its protected natural landscapes and historical use as a royal and political hunting ground.
-
E.
Sachsenhausen
Sachsenhausen is a historic and culturally vibrant district of Frankfurt am Main, known for its traditional apple wine taverns, museums, and picturesque old town streets.
- 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_69ad85a8fd9c819095ecedf838d2bf1b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb5e9af608190bfb228ef99a87bb7 |
completed | March 8, 2026, 5:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b35462c69481909700f01bacdac3e1 |
completed | March 13, 2026, 12:03 a.m. |
Created at: March 8, 2026, 3:14 p.m.