Triple
T9810255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Markus Wenzel |
E238249
|
entity |
| Predicate | languageDesigned |
P20615
|
FINISHED |
| Object | Isar |
E822907
|
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: Isar | Statement: [Markus Wenzel, languageDesigned, Isar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isar Context triple: [Markus Wenzel, languageDesigned, Isar]
-
A.
Isar
The Isar is a major river in the Austrian and German Alps that flows through cities such as Munich before joining the Danube.
-
B.
Isar
chosen
Isar is a structured, human-readable proof language designed for writing formal proofs within the Isabelle interactive theorem prover.
-
C.
Isar valley
The Isar valley is a scenic river valley in Bavaria, Germany, known for its picturesque landscapes, forests, and traditional towns along the Isar River.
-
D.
Hadern
Hadern is a borough in the southwest of Munich, Germany, known for its residential character and the large Waldfriedhof cemetery.
-
E.
Brackenberg
Brackenberg is an early recorded historical name for the Brocken, the highest peak in Germany’s Harz Mountains.
- 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_69ca84defac48190abc1148804f184c1 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb220310c8190a16ca0b746f0ef7a |
completed | April 2, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5b264c88190bf16c8c32c360878 |
completed | April 5, 2026, 3:23 a.m. |
Created at: March 30, 2026, 8:30 p.m.