Triple
T9470405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deutsches Filmmuseum |
E228374
|
entity |
| Predicate | cityDistrict |
P2709
|
FINISHED |
| Object | Sachsenhausen |
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 | Statement: [Deutsches Filmmuseum, cityDistrict, Sachsenhausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sachsenhausen Context triple: [Deutsches Filmmuseum, cityDistrict, Sachsenhausen]
-
A.
Sachsenhausen
Sachsenhausen is a district or neighborhood within the town of Giengen an der Brenz in the German state of Baden-Württemberg.
-
B.
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.
-
C.
Spandau
Spandau is a western borough of Berlin, Germany, known for its historic old town, fortress, and role as an important residential and industrial district.
-
D.
Neu-Hohenschönhausen
Neu-Hohenschönhausen is a residential locality in the northeast of Berlin, known for its large prefabricated housing estates and post-war urban development.
-
E.
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.
- 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_69ca847162c48190b079076c9595513c |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7fee13a88190b4532fb92ddaf401 |
completed | April 1, 2026, 8:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d122c45ae88190a43d70300bed98da |
completed | April 4, 2026, 2:40 p.m. |
Created at: March 30, 2026, 7:53 p.m.