Triple
T21739606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German Red Cross |
E536618
|
entity |
| Predicate | hasStaffCount |
P23565
|
FINISHED |
| Object | tens of thousands of employees |
—
|
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: tens of thousands of employees | Statement: [German Red Cross, hasStaffCount, tens of thousands of employees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStaffCount Context triple: [German Red Cross, hasStaffCount, tens of thousands of employees]
-
A.
staffSize
chosen
Indicates the number of staff members associated with an entity.
-
B.
hasEmployees
Indicates that one entity employs one or more other entities as its workers or staff.
-
C.
staffingLevel
Indicates the degree or adequacy of personnel assigned to perform a particular function, task, or operation.
-
D.
hasStaffingModel
Indicates that an entity is associated with or operates under a particular staffing model or staffing approach.
-
E.
hasSupportStaff
Indicates that an entity is associated with one or more staff members who provide assistance or support services to it.
- 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_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a714c208190b96efe23ed3bf0db |
completed | April 28, 2026, 2:24 a.m. |
| PD | Predicate disambiguation | batch_69e6969c16fc8190b5126c169317d85d |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:49 p.m.