Triple
T2211823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viktor Knavs |
E50933
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Viktor Knavs |
E50933
|
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: Viktor Knavs | Statement: [Viktor Knavs, name, Viktor Knavs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viktor Knavs Context triple: [Viktor Knavs, name, Viktor Knavs]
-
A.
Viktor Knavs
chosen
Viktor Knavs is a Slovenian former car salesman best known as the father of former U.S. First Lady Melania Trump.
-
B.
Victor Slezak
Victor Slezak is an American actor known for his work in film, television, and theater, including roles in dramas such as "The Bridges of Madison County."
-
C.
Victor Kubicek
Victor Kubicek is a film producer best known for co-producing the science fiction action movie "Terminator Salvation."
-
D.
John Kundla
John Kundla was a Hall of Fame American basketball coach best known for leading the Minneapolis Lakers to multiple early NBA championships.
-
E.
Dietrich Hrabak
Dietrich Hrabak was a German Luftwaffe fighter ace and high-ranking officer during World War II, known for his leadership roles on the Eastern Front.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfecea6c8190b762bbfda8490e31 |
completed | March 7, 2026, 6:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae655245c48190a37f4b6344a9a3dc |
completed | March 9, 2026, 6:14 a.m. |
Created at: March 4, 2026, 7:46 p.m.