Triple
T17852653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Kross |
E445847
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | David Kroß |
—
|
NE NERFINISHED |
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: David Kroß | Statement: [David Kross, birthName, David Kroß]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Kroß Context triple: [David Kross, birthName, David Kroß]
-
A.
David Kross
chosen
David Kross is a German actor best known internationally for his acclaimed performance as the young Michael Berg in the film "The Reader."
-
B.
David Krueger
David Krueger is an AI researcher and entrepreneur best known as a co-founder of the safety-focused artificial intelligence company Anthropic.
-
C.
Scott Kroopf
Scott Kroopf is an American film producer known for his work on numerous Hollywood features, including the science-fiction adventure film "Zathura: A Space Adventure."
-
D.
Ken Krueger
Ken Krueger was an American publisher and bookseller best known as a pioneering figure in comics fandom and a key organizer in the early development of major comic conventions.
-
E.
Alex Kerner
Alex Kerner is the idealistic young protagonist of the German film "Good Bye, Lenin!", who stages an elaborate ruse to protect his fragile mother from learning about the fall of East Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f26f18819089c9e43250bee6ae |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4900113f881908859f212c6ca3d9b |
completed | April 19, 2026, 8:19 a.m. |
Created at: April 10, 2026, 10:17 a.m.