Triple
T6634970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scoop |
E150424
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Kätchen |
E503529
|
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: Kätchen | Statement: [Scoop, character, Kätchen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kätchen Context triple: [Scoop, character, Kätchen]
-
A.
Gänseliesel
Gänseliesel is a famous fountain statue in Göttingen, Germany, traditionally kissed by newly graduated students and considered one of the city’s most beloved landmarks.
-
B.
Hansel & Gretel
Hansel & Gretel is a popular ballet adaptation of the classic Brothers Grimm fairy tale about two siblings who outwit a witch in a magical forest.
-
C.
Kleine Emme
Kleine Emme is a river in central Switzerland that flows through the canton of Lucerne before joining the Reuss River.
-
D.
Ännchen von Tharau
chosen
Ännchen von Tharau is the heroine of a famous 17th-century German-language love song and folk ballad, celebrated in East Prussian and Baltic cultural tradition.
-
E.
Otto the Child
Otto the Child was a 13th-century German nobleman of the Welf dynasty who became the first Duke of Brunswick-Lüneburg and a key regional ruler in northern Germany.
- 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_69c687f0ceb08190bf40807bfc605fa5 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6afcc1c9c819087fcde19a5d49fd2 |
completed | March 27, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbf71874819080cc89b6740b1567 |
completed | March 27, 2026, 6:27 p.m. |
Created at: March 27, 2026, 1:59 p.m.