Triple
T32680471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Péter Nagy |
E835567
|
entity |
| Predicate | hasNameOrderInInternationalUse |
P28784
|
FINISHED |
| Object | given name followed by family 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: given name followed by family name | Statement: [Péter Nagy, hasNameOrderInInternationalUse, given name followed by family name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNameOrderInInternationalUse Context triple: [Péter Nagy, hasNameOrderInInternationalUse, given name followed by family name]
-
A.
hasNameOrder
chosen
Indicates the specific sequence or arrangement in which the components of a name (e.g., given name, family name, titles) are ordered.
-
B.
hasInternationalName
Indicates that an entity is associated with a name used or recognized in an international or cross-linguistic context.
-
C.
givenNameUsageCountry
Indicates the country in which a particular given name is used or commonly found.
-
D.
hasNameUsageCountry
Indicates that a particular name is used or recognized within a specified country.
-
E.
hasOfficialNameInLatin
Indicates that an entity has an official or formally recognized name expressed in the Latin language.
- 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_69fe91383a1c81909266e40c3c3ede6c |
completed | May 9, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69fe8fde094081908f0f121664fbb5c7 |
completed | May 9, 2026, 1:37 a.m. |
Created at: May 1, 2026, 1:09 a.m.