Triple
T13889573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BPS-7 |
E333933
|
entity |
| Predicate | commonDepartments |
P111894
|
FINISHED |
| Object | education department |
—
|
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: education department | Statement: [BPS-7, commonDepartments, education department]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonDepartments Context triple: [BPS-7, commonDepartments, education department]
-
A.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
-
B.
worksInDepartment
Indicates that an entity is employed in and performs their job duties within a particular department.
-
C.
laterDepartment
Indicates that one department occurs or is considered after another in a defined ordering or sequence.
-
D.
basedInDepartment
Indicates that an entity operates or has its primary affiliation within a specific department.
-
E.
department
Indicates that one entity functions as an organizational unit or division within another, typically larger, entity.
- 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_69d81c5dd2d48190b7a5fc1e009de936 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de23a3a24881908d81d634622fbbcc |
completed | April 14, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69dd464b1ab48190ae50bfc902bf6ef7 |
completed | April 13, 2026, 7:38 p.m. |
| PDg | Predicate description generation | batch_69de01ed2098819088ec45069f6f2609 |
completed | April 14, 2026, 8:59 a.m. |
Created at: April 9, 2026, 10:15 p.m.