Triple
T14865699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Budapest city government |
E349609
|
entity |
| Predicate | hasCentralOfficeType |
P5164
|
FINISHED |
| Object | mayor’s cabinet |
—
|
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: mayor’s cabinet | Statement: [Budapest city government, hasCentralOfficeType, mayor’s cabinet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCentralOfficeType Context triple: [Budapest city government, hasCentralOfficeType, mayor’s cabinet]
-
A.
hasOfficeType
chosen
Indicates that an entity’s office is classified as a specific type or category of office.
-
B.
hasBranchOffice
Indicates that one organization maintains a branch office or subsidiary location in another place or entity.
-
C.
hasCenterType
Indicates that something is characterized by or assigned a specific type or category of center.
-
D.
hasCentralUnit
Indicates that an entity possesses or is associated with a primary controlling or coordinating unit.
-
E.
hasBusinessCenter
Indicates that an entity includes, contains, or is equipped with a business center facility.
- 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5761c688190b4477cb081554b51 |
completed | April 15, 2026, 12:01 a.m. |
| PD | Predicate disambiguation | batch_69de8c1798c08190b433e9ad21e41a42 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:55 a.m.