Triple
T14755055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stubentor |
E346708
|
entity |
| Predicate | hasNearbyStreet |
P8235
|
FINISHED |
| Object | Stubenring |
E468704
|
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: Stubenring | Statement: [Stubentor, hasNearbyStreet, Stubenring]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stubenring Context triple: [Stubentor, hasNearbyStreet, Stubenring]
-
A.
Stubenring
chosen
Stubenring is a prominent section of Vienna’s historic Ringstrasse boulevard, known for its grand architecture and important public buildings.
-
B.
Scheibenhof
Scheibenhof is a locality or district that forms part of the city of Krems an der Donau in Lower Austria.
-
C.
Sternschanze
Sternschanze is a trendy Hamburg neighborhood known for its alternative culture, vibrant nightlife, and street art.
-
D.
Schubertring
Schubertring is a section of Vienna’s historic Ringstrasse boulevard, known for its elegant 19th-century architecture and proximity to major cultural landmarks.
-
E.
Sendlinger Tor
Sendlinger Tor is a historic city gate in Munich, Germany, and one of the remaining medieval entrances to the old town.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7d59df08190a86da5048358bd6e |
completed | April 14, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdfb9e1b8481909abea3daabe91302 |
completed | May 8, 2026, 3:05 p.m. |
Created at: April 10, 2026, 1:30 a.m.