Triple
T11486131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ines Knauss |
E272282
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Knauss |
E707046
|
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: Knauss | Statement: [Ines Knauss, familyName, Knauss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Knauss Context triple: [Ines Knauss, familyName, Knauss]
-
A.
Knauss
chosen
Knauss is a German-language surname, notably borne in a Slovene form by Melania Trump’s birth family.
-
B.
Kavli
Kavli is a surname most prominently associated with Norwegian-American entrepreneur and philanthropist Fred Kavli, known for founding the Kavli Foundation to support scientific research.
-
C.
Knudtson
Knudtson is a surname most notably associated with American film editor Frederic Knudtson.
-
D.
Kupferberg
Kupferberg is a small town in the Bavarian region of Germany, known for its historical charm and location within the Franconian landscape.
-
E.
Kahn-Ackermann
Kahn-Ackermann is a German surname most notably borne by the politician and diplomat Georg Kahn-Ackermann.
- 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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85a1fc9688190aacc2eed64229b79 |
completed | April 10, 2026, 2:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6046076d0819087766bf905825217 |
completed | April 20, 2026, 10:48 a.m. |
Created at: April 8, 2026, 9:36 p.m.