Triple
T12525896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruß |
E299438
|
entity |
| Predicate | hasOriginalTitle |
P38
|
FINISHED |
| Object | Ruß |
E299438
|
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: Ruß | Statement: [Ruß, hasOriginalTitle, Ruß]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ruß Context triple: [Ruß, hasOriginalTitle, Ruß]
-
A.
Ruß
chosen
"Ruß" is a literary work by contemporary German-Turkish author Feridun Zaimoglu, known for its exploration of identity, migration, and marginalized voices in German society.
-
B.
Rusa
Rusa is a genus of deer native to South and Southeast Asia, including species such as the Javan rusa and sambar.
-
C.
Russas
Russas is a municipality in the northeastern Brazilian state of Ceará, known for its agricultural activities and semi-arid climate.
-
D.
Belorusskaya
Belorusskaya is a Moscow Metro station that serves as a key transport hub and interchange point near Belorussky railway terminal.
-
E.
Kubinka
Kubinka is a town in Moscow Oblast, Russia, best known for its large military airbase and the renowned Kubinka Tank Museum.
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9545d7e6c819080c3a85c18caa1ae |
completed | April 10, 2026, 7:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65eae71fc819083ec63dbfbf32465 |
completed | May 2, 2026, 8:29 p.m. |
Created at: April 8, 2026, 9:57 p.m.