Triple
T15406848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Käthe Boelcke |
E368480
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Käthe |
E797388
|
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: Käthe | Statement: [Käthe Boelcke, givenName, Käthe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Käthe Context triple: [Käthe Boelcke, givenName, Käthe]
-
A.
Käthe
chosen
Käthe is a German given name most famously borne by the expressionist artist and printmaker Käthe Kollwitz.
-
B.
Bettina
Bettina is a feminine given name of Hebrew origin, often considered a diminutive of Elisabeth or Benedetta and used in various European languages.
-
C.
Elfriede
Elfriede is a feminine given name of German origin, notably borne by Austrian Nobel Prize–winning writer Elfriede Jelinek.
-
D.
Beate
Beate is a feminine given name used in various European countries, particularly in German-speaking regions.
-
E.
Emma Kunz
Emma Kunz was a Swiss healer, researcher, and artist known for her geometric drawings and work with natural remedies, particularly the healing rock AION A.
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ea36c6881909eaea48e9608897a |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff135a26f08190ad3fc1d5a263a24e |
completed | May 9, 2026, 10:58 a.m. |
Created at: April 10, 2026, 3:20 a.m.