Triple
T13336898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sorbian people |
E317716
|
entity |
| Predicate | hasCapitalOfCulturalLife |
P61272
|
FINISHED |
| Object | Cottbus |
E203175
|
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: Cottbus | Statement: [Sorbian people, hasCapitalOfCulturalLife, Cottbus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cottbus Context triple: [Sorbian people, hasCapitalOfCulturalLife, Cottbus]
-
A.
Cottbus
chosen
Cottbus is a city in eastern Germany known as a regional center for science and technology, including aerospace research.
-
B.
Magdeburg
Magdeburg is a historic city in central Germany, known for its medieval cathedral, role as a major trading and industrial center, and location on the Elbe River.
-
C.
Chemnitz
Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
-
D.
Hoyerswerda
Hoyerswerda is a town in eastern Germany’s Saxony region, historically shaped by lignite mining and now known for its proximity to the emerging Lusatian lake landscape.
-
E.
Leipzig
Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dadcd4cb008190af99c4856e76ac08 |
completed | April 11, 2026, 11:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5da6a2081909dcc9785598e1196 |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 9, 2026, 9:31 p.m.