Triple
T20056693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tarschys |
E499355
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Daniel Tarschys |
—
|
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: Daniel Tarschys | Statement: [Tarschys, hasNotableBearer, Daniel Tarschys]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Tarschys Context triple: [Tarschys, hasNotableBearer, Daniel Tarschys]
-
A.
Daniel Tarschys
chosen
Daniel Tarschys is a Swedish political scientist and politician who served as Secretary General of the Council of Europe in the 1990s.
-
B.
Matthias Grunsky
Matthias Grunsky is an Austrian cinematographer known for his long-time collaboration with director Andrew Bujalski on acclaimed independent films.
-
C.
Daniel Zaidenstadt
Daniel Zaidenstadt is a professional audio engineer known for his work as an assistant engineer on major hip-hop and pop recordings.
-
D.
Daniel Taplitz
Daniel Taplitz is a film screenwriter and director known for his work on feature films such as "Red Dog."
-
E.
Stefan Czapsky
Stefan Czapsky is an American cinematographer best known for his visually distinctive work on films such as Tim Burton’s "Edward Scissorhands" and "Batman Returns."
- 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_69da6276bcf48190aabbf279192a5fb4 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e663337d0c8190b82422802396cd67 |
completed | April 20, 2026, 5:32 p.m. |
Created at: April 11, 2026, 3:38 p.m.