Triple
T5074474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maribor |
E114358
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Marburg, Germany |
E46876
|
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, Germany | Statement: [Maribor, hasTwinTown, Marburg, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marburg, Germany Context triple: [Maribor, hasTwinTown, Marburg, Germany]
-
A.
Marburg, Hesse, Germany
chosen
Marburg is a historic university town in the German state of Hesse, known for its medieval architecture and its long-standing role as a center of scientific and academic research.
-
B.
Schröttinghausen, Germany
Schröttinghausen is a small locality in Germany best known as the birthplace of influential astronomer Walter Baade.
-
C.
Friedberg, Germany
Friedberg, Germany is a historic town in the state of Hesse known for its medieval architecture, including a well-preserved castle and old town center.
-
D.
Hamm, Germany
Hamm is a city in the German state of North Rhine-Westphalia, known as an industrial and transportation hub in the eastern Ruhr area.
-
E.
Giessen, Germany
Giessen, Germany is a central German university town in the state of Hesse, known for its large student population and academic institutions.
- 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_69bd443cf28c8190ad371d603563dbdd |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd74d0be1c819081b26235fe602a30 |
completed | March 20, 2026, 4:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba689ee081909a6bb75c6da07db5 |
completed | March 21, 2026, 3:34 p.m. |
Created at: March 20, 2026, 1:39 p.m.