Triple
T10137660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landessynode |
E226901
|
entity |
| Predicate | meetsAt |
P373
|
FINISHED |
| Object | Bayern |
E7752
|
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: Bayern | Statement: [Landessynode, meetsAt, Bayern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayern Context triple: [Landessynode, meetsAt, Bayern]
-
A.
Bavaria
chosen
Bavaria is a historic region and federal state in southeastern Germany, known for its distinct cultural traditions, large size and population, and major cities such as Munich.
-
B.
Swabia (Bavaria)
Swabia (Bavaria) is an administrative region in southwestern Bavaria, Germany, known for its distinct Swabian cultural heritage and mix of industrial cities and rural landscapes.
-
C.
Bavier
Bavier is the surname of Frances Bavier, the American actress best known for playing Aunt Bee on the classic television series "The Andy Griffith Show."
-
D.
Hesse
Hesse is a federal state in central Germany known for its financial hub Frankfurt am Main and its mix of urban centers, forests, and historic towns.
-
E.
Rübeland
Rübeland is a village in the Harz Mountains of central Germany, known for its show caves and scenic natural surroundings.
- 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_69ca8433ec308190b8b25a6fe359c34c |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cde881a2188190a5e519b90a1b910b |
completed | April 2, 2026, 3:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e5ee7b6081909f5c08583a619308 |
completed | April 5, 2026, 10:45 p.m. |
Created at: March 30, 2026, 9:06 p.m.