Triple
T287734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State Bank of the USSR |
E5920
|
entity |
| Predicate | hadDepartment |
P1467
|
FINISHED |
| Object | central office in Moscow |
—
|
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: central office in Moscow | Statement: [State Bank of the USSR, hadDepartment, central office in Moscow]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadDepartment Context triple: [State Bank of the USSR, hadDepartment, central office in Moscow]
-
A.
department
chosen
Indicates that one entity functions as an organizational unit or division within another, typically larger, entity.
-
B.
hadMember
Indicates that an entity was formerly a member or part of another entity or group.
-
C.
hadFort
Indicates that an entity possessed, controlled, or contained a fort at some time.
-
D.
hasAcademicDepartment
Indicates that an institution or organization includes or is associated with a specific academic department.
-
E.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
- 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_69a25946a7ac8190a78871c210213272 |
completed | Feb. 28, 2026, 2:56 a.m. |
| NER | Named-entity recognition | batch_69a25e2ddaa88190b08c40b5823f30a0 |
completed | Feb. 28, 2026, 3:17 a.m. |
| PD | Predicate disambiguation | batch_69a25b7c1448819082064f474633acd5 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 3:02 a.m.