Triple
T19407549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ossiacher See |
E485500
|
entity |
| Predicate | hasResort |
P4287
|
FINISHED |
| Object | Bodensdorf |
—
|
NE NERFINISHED |
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: Bodensdorf | Statement: [Ossiacher See, hasResort, Bodensdorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bodensdorf Context triple: [Ossiacher See, hasResort, Bodensdorf]
-
A.
Bodensdorf
chosen
Bodensdorf is a lakeside village in Carinthia, Austria, known as a holiday resort on the northern shore of Lake Ossiach.
-
B.
Biendorf
Biendorf is a small municipality in northern Germany notable as the birthplace of German Field Marshal Helmuth von Moltke the Younger.
-
C.
Landersdorf
Landersdorf is a locality within the city of Krems an der Donau in Lower Austria, known as part of its surrounding wine-growing and rural area.
-
D.
Bohnsdorf
Bohnsdorf is a residential locality in the southeastern part of Berlin, Germany, known for its suburban character and proximity to the city’s green and lake-rich areas.
-
E.
Burkhardtsdorf
Burkhardtsdorf is a small municipality in the Erzgebirge (Ore Mountains) region of Saxony, eastern Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d5162481909db12435d9535c1a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6257bad0c819088dd7729b6a36a94 |
completed | April 20, 2026, 1:09 p.m. |
Created at: April 10, 2026, 1:36 p.m.