Triple
T32680470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Péter Nagy |
E835567
|
entity |
| Predicate | hasNameOrderInHungarian |
P173913
|
FINISHED |
| Object | family name followed by given name |
—
|
LITERAL 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: family name followed by given name | Statement: [Péter Nagy, hasNameOrderInHungarian, family name followed by given name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNameOrderInHungarian Context triple: [Péter Nagy, hasNameOrderInHungarian, family name followed by given name]
-
A.
nameOrderInHungarian
chosen
Indicates the conventional ordering of personal name components as used in Hungarian (typically family name followed by given name).
-
B.
hasGivenNameFormInHungarian
Indicates that an entity’s given name is expressed in its specific Hungarian linguistic form.
-
C.
nameInHungarian
Indicates that one entity is the Hungarian-language name or designation of another entity.
-
D.
hasNameOrder
Indicates the specific sequence or arrangement in which the components of a name (e.g., given name, family name, titles) are ordered.
-
E.
orderNameInLatin
Indicates that an entity’s order name is specified or represented in Latin.
- F. None of above.
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_69f3493134b48190aa3c8cb523bd3800 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fe8f74748c8190bd14a856c057f9f7 |
completed | May 9, 2026, 1:35 a.m. |
| PD | Predicate disambiguation | batch_69fe8e7ed8088190929e0df67aca4de9 |
completed | May 9, 2026, 1:31 a.m. |
Created at: May 1, 2026, 1:09 a.m.