Triple
T16079861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friedrich Karl von Savigny |
E390075
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Marburg |
E174796
|
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: Marburg | Statement: [Friedrich Karl von Savigny, workLocation, Marburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marburg Context triple: [Friedrich Karl von Savigny, workLocation, Marburg]
-
A.
Marburg
chosen
Marburg is a historic university town in central Germany known for its well-preserved medieval old town and the Philipps-Universität, one of the oldest Protestant universities in the world.
-
B.
Marburg
Marburg is a small rural township in Queensland, Australia, known for its historic buildings and location between Ipswich and Toowoomba.
-
C.
Diemelstadt
Diemelstadt is a small town in the German state of North Rhine-Westphalia, known for its rural character and location near the Diemel River.
-
D.
Vienenburg
Vienenburg is a district of Goslar in Lower Saxony, Germany, known for its historic town center and proximity to the Harz Mountains.
-
E.
Riemst
Riemst is a municipality in the Belgian province of Limburg, known for its rural character and location near the borders with the Netherlands and Germany.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18448bebc8190b0e84b1da097bf8b |
completed | April 17, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe48adec081909623355eabee472c |
completed | May 10, 2026, 1:51 a.m. |
Created at: April 10, 2026, 4:57 a.m.