Triple
T1404700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | President of Hungary |
E31664
|
entity |
| Predicate | officeHolderTitleInHungarian |
P27627
|
FINISHED |
| Object | Magyarország köztársasági elnöke |
—
|
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: Magyarország köztársasági elnöke | Statement: [President of Hungary, officeHolderTitleInHungarian, Magyarország köztársasági elnöke]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeHolderTitleInHungarian Context triple: [President of Hungary, officeHolderTitleInHungarian, Magyarország köztársasági elnöke]
-
A.
officeHolderTitle
Indicates the official position or title held by a person in an office or role.
-
B.
chancellorTitle
Indicates the official title or designation held by an individual serving in the role of chancellor.
-
C.
headOfOffice
Indicates that one entity serves as the chief or leading authority in charge of a particular office or organizational unit.
-
D.
officeHolderTitleInPortuguese
Indicates the official title or designation of an office holder as expressed in the Portuguese language.
-
E.
officeHolderOf
Indicates that a person holds or has held an official position or role within a specified organization, institution, or office.
- F. None of above. chosen
Provenance (4 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_69a49918e1f88190ba610f9dc8114578 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3bb3a9c81909db2ad91defd87b6 |
completed | March 1, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69a4bf030a388190bc82d30b9233e873 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c11067b48190bca6ef3ac1475c20 |
completed | March 1, 2026, 10:43 p.m. |
Created at: March 1, 2026, 7:59 p.m.