Triple
T9413982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | district of Rosenheim |
E226767
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Grassau |
E800311
|
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: Grassau | Statement: [district of Rosenheim, contains, Grassau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grassau Context triple: [district of Rosenheim, contains, Grassau]
-
A.
Grassau
chosen
Grassau is a Bavarian town in southern Germany, known for its scenic location near the Alps and the Chiemsee lake in the Chiemgau region.
-
B.
Gaissau
Gaissau is a small municipality in the Austrian state of Vorarlberg, located near the Rhine River and the border with Switzerland.
-
C.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
-
D.
Ochsenfeld
Ochsenfeld is a German surname most notably borne by physicist Robert Ochsenfeld, known for his work on superconductivity.
-
E.
Tussenhausen
Tussenhausen is a municipality in the district of Unterallgäu in Bavaria, Germany, known for its rural character and small villages such as Mattsies.
- 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_69d1611c908c8190aea6768e5659c3ca |
completed | April 4, 2026, 7:06 p.m. |
Created at: March 30, 2026, 7:47 p.m.