Triple
T35247782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Office of the President of Ukraine building |
E1018014
|
entity |
| Predicate | hasCabinetRoom |
P200395
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Office of the President of Ukraine building, hasCabinetRoom, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCabinetRoom Context triple: [Office of the President of Ukraine building, hasCabinetRoom, yes]
-
A.
hasCabinet
Indicates that one entity possesses, includes, or is equipped with a cabinet associated with it.
-
B.
hasCabinetName
Indicates that an entity is associated with a specific cabinet by its name.
-
C.
isInCabinet
Indicates that one entity serves as a member of the governing cabinet associated with another entity (such as a government or administration).
-
D.
hasCabinetRole
Indicates that an entity holds or is assigned a specific role or position within a cabinet (such as a governmental or executive cabinet).
-
E.
hasCabinetStatus
Indicates that an entity holds a specific status or role within a cabinet-level governing body.
- 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_69f76de407d081909dfc3c419817ae93 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ff878f41888190bcb3bc41ad26081a |
completed | May 9, 2026, 7:14 p.m. |
| PD | Predicate disambiguation | batch_69ff854082d88190aad3bfedf05e849f |
completed | May 9, 2026, 7:04 p.m. |
| PDg | Predicate description generation | batch_69ff878e8334819097e3c4bb5ca6ffa5 |
completed | May 9, 2026, 7:14 p.m. |
Created at: May 3, 2026, 4:02 p.m.