Triple
T3381698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin Tegel Airport |
E71199
|
entity |
| Predicate | servedCity |
P3936
|
FINISHED |
| Object | Eberswalde |
E237109
|
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: Eberswalde | Statement: [Berlin Tegel Airport, servedCity, Eberswalde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eberswalde Context triple: [Berlin Tegel Airport, servedCity, Eberswalde]
-
A.
Eberswalde
chosen
Eberswalde is a town in northeastern Germany known for its industrial heritage and surrounding forests, located northeast of Berlin.
-
B.
Golm
Golm is a hill on the island of Usedom in Germany, known both for its elevation and for the large World War II war cemetery located on its slopes.
-
C.
Neubukow
Neubukow is a small town in northern Germany best known as the birthplace of archaeologist Heinrich Schliemann.
-
D.
Maadi
Maadi is a suburban district in southern Cairo, Egypt, known for its leafy residential streets, expatriate community, and proximity to the Nile.
-
E.
Duderstadt
Duderstadt is a historic small town in southern Lower Saxony, Germany, known for its well-preserved medieval timber-framed architecture and role as a regional center in the Eichsfeld area.
- 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_69b3344f9b448190aab1038ead60fa48 |
completed | March 12, 2026, 9:46 p.m. |
Created at: March 8, 2026, 3:14 p.m.