Triple
T10129989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Markkleeberg |
E226311
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Saxony |
E11465
|
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: Saxony | Statement: [Markkleeberg, locatedIn, Saxony]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saxony Context triple: [Markkleeberg, locatedIn, Saxony]
-
A.
Saxony
chosen
Saxony is a historic region and former kingdom in eastern Germany, known for its cultural centers like Dresden and Leipzig and its significant role in Central European history.
-
B.
Thuringia
Thuringia is a federal state in central Germany known for its forested landscapes, historic cities like Weimar and Erfurt, and its rich cultural and intellectual heritage.
-
C.
Brandenburg
Brandenburg is a federal state in northeastern Germany that surrounds Berlin and is known for its lakes, forests, and historic Prussian heritage.
-
D.
Brandenburg
Brandenburg is a small city in Meade County, Kentucky, situated along the Ohio River and serving as the county seat.
-
E.
Bavaria
Bavaria is a historic region and federal state in southeastern Germany, known for its distinct cultural traditions, large size and population, and major cities such as Munich.
- 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_69ca843057b48190a86730167f5d6b98 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cdd33438988190be45878f98695816 |
completed | April 2, 2026, 2:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d3171f23d08190a5f9cd2c3d139a0e |
completed | April 6, 2026, 2:14 a.m. |
Created at: March 30, 2026, 9:05 p.m.