Triple
T11039051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | László Kovács |
E260960
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | László |
E226387
|
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: László | Statement: [László Kovács, givenName, László]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: László Context triple: [László Kovács, givenName, László]
-
A.
László
chosen
László is a Hungarian given name most famously borne by the avant-garde artist and Bauhaus teacher László Moholy-Nagy.
-
B.
Lajos
Lajos is a Hungarian masculine given name commonly used in Central and Eastern Europe.
-
C.
István
István is the Hungarian given name of Stephen I of Hungary, the first Christian king and founder of the medieval Hungarian state.
-
D.
Ernő
Ernő is a Hungarian-born British modernist architect best known for his influential and often controversial Brutalist buildings in London.
-
E.
Gábor
Gábor is a Hungarian masculine given name, commonly used as the local form of Gabriel.
- 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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797fe93b081909d58bfd4b42715f0 |
completed | April 9, 2026, 12:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3c846d9f08190943d457ff6da6a9f |
completed | April 18, 2026, 6:07 p.m. |
Created at: April 8, 2026, 9:26 p.m.