Triple
T12496289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanak Sprak |
E298693
|
entity |
| Predicate | language |
P15
|
FINISHED |
| Object | German |
E9053
|
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: German | Statement: [Kanak Sprak, language, German]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: German Context triple: [Kanak Sprak, language, German]
-
A.
German
German refers to a person belonging to the ethnic group native to Germany, typically associated with the German language and culture.
-
B.
German
chosen
German is a West Germanic language widely spoken in Central Europe and used as an official language in several countries, including Germany, Austria, Switzerland, and Luxembourg.
-
C.
Deutch
Deutch is a surname most notably associated with John M. Deutch, an American chemist, academic, and former Director of Central Intelligence.
-
D.
Tyskie
Tyskie is a popular Polish beer brand known for its pale lagers and long brewing tradition.
-
E.
Tedesco
Tedesco is an Italian-origin surname borne by various notable individuals in fields such as politics, sports, and the arts.
- 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_69d6ada4cd388190ae3bbf83ff87057a |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94df948308190ace333230a4a3b38 |
completed | April 10, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64badad488190ae1c6c2883a88a4b |
completed | May 2, 2026, 7:08 p.m. |
Created at: April 8, 2026, 9:57 p.m.