Triple
T7141503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langendorf |
E166452
|
entity |
| Predicate | district |
P2709
|
FINISHED |
| Object | Lebern District |
E511739
|
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: Lebern District | Statement: [Langendorf, district, Lebern District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lebern District Context triple: [Langendorf, district, Lebern District]
-
A.
Lebern District
chosen
Lebern District is an administrative district in the canton of Solothurn in northwestern Switzerland.
-
B.
Mohrungen district
Mohrungen district was a former administrative district in East Prussia, centered around the town of Mohrungen (now Morąg in Poland), that existed under German rule until the end of World War II.
-
C.
Karlstein district
Karlstein district is a neighborhood of the spa town Bad Reichenhall in Bavaria, Germany, known for its scenic Alpine setting and historic character.
-
D.
Hof district
Hof district is a rural administrative district in the Bavarian region of Upper Franconia in Germany, known for its small towns, agricultural areas, and proximity to the Czech border.
-
E.
Dukenburg district
Dukenburg district is a residential area in the southwestern part of Nijmegen, Netherlands, characterized by post-war housing estates, green spaces, and local shopping facilities.
- 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_69c6888579d481909e05a8d6b81bf733 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e778875c8190a5202d3efe5a842d |
completed | March 27, 2026, 8:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7a34e55f481909b1aee270363fd61 |
completed | March 28, 2026, 9:45 a.m. |
Created at: March 27, 2026, 2:45 p.m.