Triple
T9413965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | district of Rosenheim |
E226767
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Chiemsee |
E41133
|
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: Chiemsee | Statement: [district of Rosenheim, contains, Chiemsee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chiemsee Context triple: [district of Rosenheim, contains, Chiemsee]
-
A.
Chiemsee
chosen
Chiemsee is one of Germany’s largest lakes, famed for its scenic Alpine setting and historic islands such as Herrenchiemsee with its royal palace.
-
B.
Altmühlsee
Altmühlsee is an artificial recreational lake in Bavaria, Germany, popular for swimming, sailing, and nature conservation.
-
C.
Tegernsee
Tegernsee is a picturesque alpine lake in southern Germany renowned for its clear waters, surrounding mountains, and popular spa and resort towns.
-
D.
Königssee
Königssee is a picturesque alpine lake in southeastern Germany, renowned for its emerald-green waters, steep surrounding mountains, and status as one of the cleanest lakes in the country.
-
E.
Ammersee
Ammersee is a large glacial lake in southern Germany known for its scenic shores, recreational activities, and proximity to the Alps.
- 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_69ca843280488190bc65600e843ef9e6 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd68c680e48190be82e3829e8711f0 |
completed | April 1, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94aceb7108190beeec78587c04161 |
completed | April 10, 2026, 7:09 p.m. |
Created at: March 30, 2026, 7:47 p.m.