Triple
T11912792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Währing |
E283435
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Alsergrund |
E348815
|
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: Alsergrund | Statement: [Währing, borderedBy, Alsergrund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alsergrund Context triple: [Währing, borderedBy, Alsergrund]
-
A.
Alsergrund
chosen
Alsergrund is the 9th district of Vienna, Austria, known for its historic architecture, cultural institutions, and proximity to the city center.
-
B.
Brigittenau
Brigittenau is the 20th district of Vienna, Austria, known for its dense urban character and location between the Danube Canal and the Danube River.
-
C.
Bergmannkiez
Bergmannkiez is a popular, lively neighborhood in Berlin known for its historic architecture, café-lined streets, and vibrant cultural scene.
-
D.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
E.
Scheibenhof
Scheibenhof is a locality or district that forms part of the city of Krems an der Donau in Lower Austria.
- 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_69d6ab2c07e88190ba13b0d21fd6cf33 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8e528f6748190ac873a040a61fa93 |
completed | April 10, 2026, 11:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f4185fd3588190807cc2906c4ce3fd |
completed | May 1, 2026, 3:05 a.m. |
Created at: April 8, 2026, 9:44 p.m.