Triple
T4933888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tegeler Werder |
E110762
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Tegeler Werder |
E110762
|
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: Tegeler Werder | Statement: [Tegeler Werder, hasName, Tegeler Werder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tegeler Werder Context triple: [Tegeler Werder, hasName, Tegeler Werder]
-
A.
Tegeler Werder
chosen
Tegeler Werder is a wooded island located in Lake Tegel in Berlin, Germany, known as part of the lake’s natural landscape and recreational area.
-
B.
Reinickendorf
Reinickendorf is a borough in the northwest of Berlin, Germany, known for its mix of residential neighborhoods, industrial areas, and green spaces including parts of Lake Tegel.
-
C.
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.
-
D.
Treptow-Köpenick
Treptow-Köpenick is Berlin’s largest and greenest borough, known for its extensive forests, lakes, and historic town centers such as Köpenick.
-
E.
Wilmersdorf
Wilmersdorf is a residential district in southwestern Berlin known for its affluent neighborhoods, shopping streets like Kurfürstendamm, and a mix of historic and modern architecture.
- 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_69bd4415190c8190817bee7ec9f9f944 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7066ed548190a76a9559f90e3869 |
completed | March 20, 2026, 4:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be77b41c4c8190b4f714334242bc9b |
completed | March 21, 2026, 10:49 a.m. |
Created at: March 20, 2026, 1:30 p.m.