Triple
T10834669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diemel |
E255721
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Warburg |
E516535
|
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: Warburg | Statement: [Diemel, passesThrough, Warburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Warburg Context triple: [Diemel, passesThrough, Warburg]
-
A.
Warburg
Warburg is a prominent German-Jewish banking and philanthropic family historically influential in international finance and economic policy.
-
B.
Warburg
chosen
Warburg is a historic small city in the German state of Hesse, known for its well-preserved medieval old town and hilltop castle.
-
C.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
D.
Hadern
Hadern is a borough in the southwest of Munich, Germany, known for its residential character and the large Waldfriedhof cemetery.
-
E.
Löwenberg
Löwenberg is a town in Germany known for its cultural and municipal partnership as a twin town of Weilburg.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d74425447081908fb51c7edf54af67 |
completed | April 9, 2026, 6:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb11a264c8190829ff89f0b13d063 |
completed | April 14, 2026, 9:26 p.m. |
Created at: April 8, 2026, 9:19 p.m.