Triple
T11646610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaiserwiese |
E276790
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Praterstern |
E56329
|
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: Praterstern | Statement: [Kaiserwiese, near, Praterstern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Praterstern Context triple: [Kaiserwiese, near, Praterstern]
-
A.
Sendlinger Tor
Sendlinger Tor is a historic city gate in Munich, Germany, and one of the remaining medieval entrances to the old town.
-
B.
Prater
chosen
Prater is a large public park and historic amusement area in Vienna, Austria, best known for its iconic Giant Ferris Wheel and extensive green spaces.
-
C.
Altkönig
Altkönig is a prominent mountain peak in Germany’s Taunus range, known for its scenic hiking trails and remains of ancient Celtic fortifications.
-
D.
Mönchsberg
Mönchsberg is a prominent wooded hill in the center of Salzburg, Austria, known for its scenic walking paths, historic fortifications, and panoramic views over the old town.
-
E.
Deutschlandsberg
Deutschlandsberg is a small Austrian town in the southwest of the state of Styria, known for its surrounding vineyards, castle, and scenic hilly landscape.
- 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_69d6aafbb3c081908a9cdb4ecb8d981d |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a2cc8bfc8190a063cc37de9596a9 |
completed | April 10, 2026, 7:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef1381a49c81909d849edbfab7448e |
completed | April 27, 2026, 7:42 a.m. |
Created at: April 8, 2026, 9:39 p.m.