Triple
T8351033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Kajetan |
E196157
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Hofgarten (Munich) |
E196494
|
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: Hofgarten (Munich) | Statement: [St. Kajetan, near, Hofgarten (Munich)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hofgarten (Munich) Context triple: [St. Kajetan, near, Hofgarten (Munich)]
-
A.
Hofgarten
chosen
The Hofgarten is a historic Renaissance-style court garden in central Munich, known for its arcades, pavilions, and role as a popular public park and cultural venue.
-
B.
Volksgarten
Volksgarten is a historic public park in central Vienna renowned for its formal rose gardens, neoclassical monuments, and location along the Ringstrasse.
-
C.
Englischer Garten
Englischer Garten is a large public park in Munich, Germany, renowned for its expansive green spaces, beer gardens, and riverside surfing on the Eisbach.
-
D.
Georgengarten
Georgengarten is a large English-style landscape park in Hanover, Germany, known for its expansive lawns, tree-lined avenues, and integration into the historic Herrenhausen Gardens ensemble.
-
E.
Tiergarten
Tiergarten is a large central park in Berlin known for its expansive green spaces, monuments, and cultural landmarks.
- 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_69ca82edd63c8190b876b8465464c5fa |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb8019fb308190a3edc744bd473a5b |
completed | March 31, 2026, 8:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cde7b2d00c8190b7df13a0853a6374 |
completed | April 2, 2026, 3:51 a.m. |
Created at: March 30, 2026, 5:59 p.m.