Triple
T2841336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fieseler |
E62474
|
entity |
| Predicate | basedIn |
P40
|
FINISHED |
| Object | Kassel, Germany |
E210960
|
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: Kassel, Germany | Statement: [Fieseler, basedIn, Kassel, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kassel, Germany Context triple: [Fieseler, basedIn, Kassel, Germany]
-
A.
Giessen, Germany
Giessen, Germany is a central German university town in the state of Hesse, known for its large student population and academic institutions.
-
B.
Schröttinghausen, Germany
Schröttinghausen is a small locality in Germany best known as the birthplace of influential astronomer Walter Baade.
-
C.
Kassel
chosen
Kassel is a city in central Germany known for its cultural institutions and as the host of the renowned contemporary art exhibition documenta.
-
D.
Krefeld, Germany
Krefeld, Germany is an industrial city in North Rhine-Westphalia known historically for its textile and silk production.
-
E.
Weinheim, Germany
Weinheim, Germany is a town in the state of Baden-Württemberg known for its historic old town, twin castles, and role as a regional economic and publishing center.
- 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_69ab4c3d16bc81908b3a1c98fbd287fe |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf16d0c08190bb8de4a4160b4414 |
completed | March 7, 2026, 8:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b01d7705108190a986d3def0c08f7b |
completed | March 10, 2026, 1:32 p.m. |
Created at: March 6, 2026, 10:01 p.m.