Triple
T8778389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lichterfelde |
E208659
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Zehlendorf |
E13910
|
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: Zehlendorf | Statement: [Lichterfelde, borderedBy, Zehlendorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zehlendorf Context triple: [Lichterfelde, borderedBy, Zehlendorf]
-
A.
Steglitz-Zehlendorf
chosen
Steglitz-Zehlendorf is a borough in southwestern Berlin known for its affluent residential areas, lakes and forests, and historically significant sites such as the Wannsee Conference villa.
-
B.
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.
-
C.
Schönewalde
Schönewalde is a town in the state of Brandenburg, Germany, known for hosting a German Air Force base.
-
D.
Charlottenburg-Wilmersdorf
Charlottenburg-Wilmersdorf is a western borough of Berlin, Germany, known for its historic city center, cultural institutions, and major sports venues.
-
E.
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.
- 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_69ca835fbee88190bf625939bac48d7f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f531bd481909d877dadf9b6e9fb |
completed | March 31, 2026, 11:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc1bbf6b881909bc154caa2fcadbe |
completed | April 3, 2026, 1:33 p.m. |
Created at: March 30, 2026, 6:42 p.m.