Triple
T6230916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S7 |
E139349
|
entity |
| Predicate | viaStation |
P64719
|
FINISHED |
| Object |
Mahlsdorf
Mahlsdorf is a locality in the borough of Marzahn-Hellersdorf in eastern Berlin, Germany, known for its residential character and historic village center.
|
E583275
|
NE FINISHED |
How this triple was built (4 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: Mahlsdorf | Statement: [S7, viaStation, Mahlsdorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mahlsdorf Context triple: [S7, viaStation, Mahlsdorf]
-
A.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
-
B.
Degendorf
Degendorf is a locality within the Bavarian town and district of Lichtenfels in Germany.
-
C.
Hohen Neuendorf
Hohen Neuendorf is a town in the German state of Brandenburg, located just north of Berlin and known as a residential suburb with access to the capital.
-
D.
Wandlitz
Wandlitz is a municipality in the German state of Brandenburg, known for its lakes, forests, and proximity to Berlin.
-
E.
Zossen
Zossen is a town in Brandenburg, Germany, historically notable as a major military command center, including serving as a key headquarters area during the Soviet occupation after World War II.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mahlsdorf Triple: [S7, viaStation, Mahlsdorf]
Generated description
Mahlsdorf is a locality in the borough of Marzahn-Hellersdorf in eastern Berlin, Germany, known for its residential character and historic village center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mahlsdorf Target entity description: Mahlsdorf is a locality in the borough of Marzahn-Hellersdorf in eastern Berlin, Germany, known for its residential character and historic village center.
-
A.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
-
B.
Degendorf
Degendorf is a locality within the Bavarian town and district of Lichtenfels in Germany.
-
C.
Hohen Neuendorf
Hohen Neuendorf is a town in the German state of Brandenburg, located just north of Berlin and known as a residential suburb with access to the capital.
-
D.
Wandlitz
Wandlitz is a municipality in the German state of Brandenburg, known for its lakes, forests, and proximity to Berlin.
-
E.
Zossen
Zossen is a town in Brandenburg, Germany, historically notable as a major military command center, including serving as a key headquarters area during the Soviet occupation after World War II.
- F. None of above. chosen
Provenance (5 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_69c008afd3148190b71e9eaa60420dd1 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062ec5be4819084d6df2e8dd2a542 |
completed | March 22, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5e3e5bd988190ad9b0af668f5b05c |
completed | March 27, 2026, 1:56 a.m. |
| NEDg | Description generation | batch_69c5e531074481909f2b9099857d7414 |
completed | March 27, 2026, 2:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c5e58034888190bd24310eff354633 |
completed | March 27, 2026, 2:03 a.m. |
Created at: March 22, 2026, 4:22 p.m.