Triple
T21749886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viktor Kassai |
E536886
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Viktor Kassai |
—
|
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: Viktor Kassai | Statement: [Viktor Kassai, name, Viktor Kassai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viktor Kassai Context triple: [Viktor Kassai, name, Viktor Kassai]
-
A.
Viktor Kassai
chosen
Viktor Kassai is a Hungarian football referee renowned for officiating high-profile international matches, including major UEFA and FIFA tournaments.
-
B.
Bruno Pésery
Bruno Pésery is a French film producer known for his work on notable art-house and auteur-driven films.
-
C.
Zoltán Latinovits
Zoltán Latinovits was a renowned Hungarian stage and film actor, often celebrated as one of the greatest Hungarian performers of the 20th century.
-
D.
Leo Janos
Leo Janos was an American writer and journalist best known for co-authoring the autobiography of legendary test pilot Chuck Yeager.
-
E.
Peter Kónya
Peter Kónya is a Slovak historian and academic who serves as the rector of the University of Prešov.
- 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a78bd908190b74e26ab1cc8788f |
completed | April 28, 2026, 2:24 a.m. |
Created at: April 16, 2026, 6:50 p.m.