Triple
T22425616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Just Cause |
E554359
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | László Kovács |
—
|
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: László Kovács | Statement: [Just Cause, cinematographyBy, László Kovács]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: László Kovács Context triple: [Just Cause, cinematographyBy, László Kovács]
-
A.
László Kovács
chosen
László Kovács was a renowned Hungarian-American cinematographer celebrated for his influential work in New Hollywood cinema, including landmark films of the late 1960s and 1970s.
-
B.
Gyula Jankovits
Gyula Jankovits was a Hungarian sculptor best known for creating prominent public monuments in Budapest, including the Gellért Monument.
-
C.
László Papp
László Papp was a legendary Hungarian boxer who became the first boxer to win three consecutive Olympic gold medals.
-
D.
László Nagy
László Nagy is a common Hungarian name shared by several notable figures, including a poet, a handball player, and a politician.
-
E.
Gábor Vajna
Gábor Vajna was a Hungarian fascist politician who served as Interior Minister in the pro-Nazi Arrow Cross regime during World War II.
- 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_69e11e4f2d0c819091aa3558ea2ee630 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a2d4dd88190a156c24b02b1591b |
completed | April 29, 2026, 1:09 a.m. |
Created at: April 16, 2026, 8:47 p.m.