Triple
T15937175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaisermühlen |
E386467
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Donaupark
Donaupark is a large public park in Vienna known for its green spaces, recreational facilities, and the prominent Danube Tower.
|
E1184448
|
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: Donaupark | Statement: [Kaisermühlen, contains, Donaupark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donaupark Context triple: [Kaisermühlen, contains, Donaupark]
-
A.
Schillerpark
Schillerpark is a historic public park in Berlin known for its expansive lawns, tree-lined paths, and role as a popular recreational area for local residents.
-
B.
Danube Park
Danube Park is a central urban green space in Novi Sad, Serbia, known for its landscaped paths, pond, and role as a popular recreational and cultural gathering spot.
-
C.
Türkenschanzpark
Türkenschanzpark is a large, historic public park in Vienna known for its landscaped hills, ponds, and diverse botanical features.
-
D.
U Kleistpark
U Kleistpark is a Berlin U-Bahn station on line U7 located in the Schöneberg district.
-
E.
Doblhoffpark
Doblhoffpark is a historic public park in Baden bei Wien, Austria, known for its extensive rose gardens, scenic ponds, and tranquil walking paths.
- 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: Donaupark Triple: [Kaisermühlen, contains, Donaupark]
Generated description
Donaupark is a large public park in Vienna known for its green spaces, recreational facilities, and the prominent Danube Tower.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Donaupark Target entity description: Donaupark is a large public park in Vienna known for its green spaces, recreational facilities, and the prominent Danube Tower.
-
A.
Schillerpark
Schillerpark is a historic public park in Berlin known for its expansive lawns, tree-lined paths, and role as a popular recreational area for local residents.
-
B.
Danube Park
Danube Park is a central urban green space in Novi Sad, Serbia, known for its landscaped paths, pond, and role as a popular recreational and cultural gathering spot.
-
C.
Türkenschanzpark
Türkenschanzpark is a large, historic public park in Vienna known for its landscaped hills, ponds, and diverse botanical features.
-
D.
U Kleistpark
U Kleistpark is a Berlin U-Bahn station on line U7 located in the Schöneberg district.
-
E.
Doblhoffpark
Doblhoffpark is a historic public park in Baden bei Wien, Austria, known for its extensive rose gardens, scenic ponds, and tranquil walking paths.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156ab7f548190b2d1aafa0e6d2c24 |
completed | April 16, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb5b8121881909b15bf6451d3d3a8 |
completed | May 9, 2026, 10:31 p.m. |
| NEDg | Description generation | batch_69ffb718d60481908ac0034ed8d8abc5 |
completed | May 9, 2026, 10:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffb7c98cf8819097c7012040dbfe89 |
completed | May 9, 2026, 10:40 p.m. |
Created at: April 10, 2026, 4:53 a.m.