Triple
T15762932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | József Rippl-Rónai |
E382142
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | József |
E865237
|
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: József | Statement: [József Rippl-Rónai, givenName, József]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: József Context triple: [József Rippl-Rónai, givenName, József]
-
A.
József
chosen
József is a Hungarian masculine given name equivalent to Joseph, commonly used in Hungary and among Hungarian communities.
-
B.
János
János is the Hungarian form of the given name John, commonly used in Hungary and among Hungarian speakers.
-
C.
Mihály
Mihály is a Hungarian given name commonly used as the equivalent of Michael in English.
-
D.
Lajos
Lajos is a Hungarian masculine given name commonly used in Central and Eastern Europe.
-
E.
László
László is a Hungarian given name most famously borne by the avant-garde artist and Bauhaus teacher László Moholy-Nagy.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e050b6c9fc8190a1bcf763c4b04b12 |
completed | April 16, 2026, 3 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeb7c2a6081908e957d39ec056062 |
completed | May 10, 2026, 2:20 a.m. |
Created at: April 10, 2026, 4:47 a.m.