Triple
T16283630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donau City |
E395330
|
entity |
| Predicate | hasNearbyLandmark |
P2064
|
FINISHED |
| Object | Donaupark |
E1184448
|
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: Donaupark | Statement: [Donau City, hasNearbyLandmark, Donaupark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donaupark Context triple: [Donau City, hasNearbyLandmark, Donaupark]
-
A.
Donaupark
chosen
Donaupark is a large public park in Vienna known for its green spaces, recreational facilities, and the prominent Danube Tower.
-
B.
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.
-
C.
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.
-
D.
Türkenschanzpark
Türkenschanzpark is a large, historic public park in Vienna known for its landscaped hills, ponds, and diverse botanical features.
-
E.
U Kleistpark
U Kleistpark is a Berlin U-Bahn station on line U7 located in the Schöneberg district.
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24912c5808190a0d9c9f491315068 |
completed | April 17, 2026, 2:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0017c8f51c8190b73cdf2834eda57f |
completed | May 10, 2026, 5:29 a.m. |
Created at: April 10, 2026, 5:05 a.m.