Triple
T6358523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ringbahn (Berlin) |
E143051
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Halensee
Halensee is a railway station in Berlin that serves the city's circular Ringbahn line, connecting the Halensee district to the wider urban rail network.
|
E588599
|
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: Halensee | Statement: [Ringbahn (Berlin), hasStation, Halensee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Halensee Context triple: [Ringbahn (Berlin), hasStation, Halensee]
-
A.
Heiligensee
Heiligensee is a residential and partly lakeside locality in the northwest of Berlin, known for its green spaces and village-like character within the borough of Reinickendorf.
-
B.
Müggelsee
Müggelsee is the largest lake in Berlin, Germany, known for its popular recreational areas and natural surroundings.
-
C.
Fleesensee
Fleesensee is a large lake in northeastern Germany known for its popular holiday resorts, water sports, and scenic natural surroundings.
-
D.
Salzgittersee
Salzgittersee is a large recreational lake in the city of Salzgitter, Germany, popular for swimming, water sports, and leisure activities.
-
E.
Schlachtensee
Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
- 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: Halensee Triple: [Ringbahn (Berlin), hasStation, Halensee]
Generated description
Halensee is a railway station in Berlin that serves the city's circular Ringbahn line, connecting the Halensee district to the wider urban rail network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Halensee Target entity description: Halensee is a railway station in Berlin that serves the city's circular Ringbahn line, connecting the Halensee district to the wider urban rail network.
-
A.
Heiligensee
Heiligensee is a residential and partly lakeside locality in the northwest of Berlin, known for its green spaces and village-like character within the borough of Reinickendorf.
-
B.
Müggelsee
Müggelsee is the largest lake in Berlin, Germany, known for its popular recreational areas and natural surroundings.
-
C.
Fleesensee
Fleesensee is a large lake in northeastern Germany known for its popular holiday resorts, water sports, and scenic natural surroundings.
-
D.
Salzgittersee
Salzgittersee is a large recreational lake in the city of Salzgitter, Germany, popular for swimming, water sports, and leisure activities.
-
E.
Schlachtensee
Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
- 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067f72f8481908f9df0c0cdf22a52 |
completed | March 22, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d5f134c8190817037ad933c4d2b |
completed | March 27, 2026, 7:10 a.m. |
| NEDg | Description generation | batch_69c62fb982088190ab4ccbd5ff23740d |
completed | March 27, 2026, 7:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6302e2f008190bd7ccdfbcddb3c07 |
completed | March 27, 2026, 7:22 a.m. |
Created at: March 22, 2026, 4:32 p.m.