Triple
T1751829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White House Office |
E38459
|
entity |
| Predicate | hasStaffType |
P121
|
FINISHED |
| Object | President’s immediate staff |
—
|
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: President’s immediate staff | Statement: [White House Office, hasStaffType, President’s immediate staff]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStaffType Context triple: [White House Office, hasStaffType, President’s immediate staff]
-
A.
hasWorkforceType
Indicates the type or category of workforce associated with an entity (such as permanent, temporary, contract, or part-time).
-
B.
hasMemberType
chosen
Indicates that an entity includes or is associated with members belonging to a specified type or category.
-
C.
hasAffiliationType
Indicates that one entity is connected to another through a specified kind or category of affiliation or association.
-
D.
hasAdministrativeType
Indicates that an entity is associated with a specific category or level of administrative classification (such as type of jurisdiction or administrative unit).
-
E.
personnelType
Indicates the classification or role category assigned to a person within an organization or system.
- 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_69a8862bdb2081908aefe831c8aa8017 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba6a63f588190b53b39c6b97d74f4 |
completed | March 7, 2026, 4:16 a.m. |
| PD | Predicate disambiguation | batch_69aa61c7ef4c8190abec87c96a787d82 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.