Triple
T5899032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sachsenhausen |
E131173
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Sachsenhausen-Süd |
E131173
|
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: Sachsenhausen-Süd | Statement: [Sachsenhausen, hasPart, Sachsenhausen-Süd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sachsenhausen-Süd Context triple: [Sachsenhausen, hasPart, Sachsenhausen-Süd]
-
A.
Sachsenhausen
chosen
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.
-
B.
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.
-
C.
Ludwigsfelde
Ludwigsfelde is a town in the German state of Brandenburg, located just south of Berlin and known for its industrial history and automotive manufacturing.
-
D.
Marzahn-Hellersdorf
Marzahn-Hellersdorf is a borough in the eastern part of Berlin, Germany, known for its large prefabricated housing estates and extensive green spaces.
-
E.
Schönewalde
Schönewalde is a town in the state of Brandenburg, Germany, known for hosting a German Air Force base.
- 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_69c00857439c819095950754176aa58a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c036f7b3f48190a499d43f8ffb2fa7 |
completed | March 22, 2026, 6:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c0076cd88190b10e51c52dcc6a2e |
completed | March 23, 2026, 4:22 a.m. |
Created at: March 22, 2026, 3:58 p.m.