Triple
T30424567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Embassy of the United Arab Emirates in Moscow |
E773988
|
entity |
| Predicate | hasLocalStaffFrom |
P192647
|
FINISHED |
| Object | Russia |
—
|
NE NERFINISHED |
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: Russia | Statement: [Embassy of the United Arab Emirates in Moscow, hasLocalStaffFrom, Russia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalStaffFrom Context triple: [Embassy of the United Arab Emirates in Moscow, hasLocalStaffFrom, Russia]
-
A.
hasInternationalStaff
Indicates that an entity employs or is associated with staff members originating from multiple countries.
-
B.
hasSupportStaff
Indicates that an entity is associated with one or more staff members who provide assistance or support services to it.
-
C.
hasLocalSupport
Indicates that an entity receives backing, endorsement, or assistance from people or organizations within its immediate geographic or community area.
-
D.
hasEmployees
Indicates that one entity employs one or more other entities as its workers or staff.
-
E.
hasLocalInstitution
Indicates that a given place or region possesses or hosts an institution that operates locally within its boundaries.
- 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_69f22491ba248190b9a4776ca8e42d02 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd231cab588190ad0953dc8f4af8f2 |
completed | May 7, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69fd1aa3f1c481909fe6e9cab1383551 |
completed | May 7, 2026, 11:05 p.m. |
| PDg | Predicate description generation | batch_69fd231bdd108190900369e07c854e95 |
completed | May 7, 2026, 11:41 p.m. |
Created at: April 29, 2026, 8:06 p.m.