Triple
T8778391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lichterfelde |
E208659
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Lankwitz |
E213752
|
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: Lankwitz | Statement: [Lichterfelde, borderedBy, Lankwitz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lankwitz Context triple: [Lichterfelde, borderedBy, Lankwitz]
-
A.
Lankwitz
chosen
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
-
B.
Zinnowitz
Zinnowitz is a seaside resort town on Germany’s Baltic Sea coast, known for its sandy beaches, historic spa architecture, and tourism on the island of Usedom.
-
C.
Markranstädt
Markranstädt is a small town in the German state of Saxony, located near Leipzig and known for its local industry and proximity to the Kulkwitzer See recreation area.
-
D.
Leisnig
Leisnig is a small historic town in the German state of Saxony, known for its medieval architecture and the prominent Mildenstein Castle overlooking the Mulde River.
-
E.
Schkopau
Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
- 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_69d054339b988190849092f178a7af2c |
completed | April 3, 2026, 11:58 p.m. |
Created at: March 30, 2026, 6:42 p.m.