Triple
T8640339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cabinet of Kenya |
E204628
|
entity |
| Predicate | numberOfCabinetSecretariesLimit |
P9266
|
FINISHED |
| Object | 22 |
—
|
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: 22 | Statement: [Cabinet of Kenya, numberOfCabinetSecretariesLimit, 22]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCabinetSecretariesLimit Context triple: [Cabinet of Kenya, numberOfCabinetSecretariesLimit, 22]
-
A.
numberOfCabinetMembers
Indicates the total count of cabinet members associated with a given government, administration, or leader.
-
B.
numberOfMinistersLimit
chosen
Indicates a constraint specifying the maximum allowable number of ministers in a given context or governing body.
-
C.
numberOfStateSecretaries
Indicates the quantity of state secretaries associated with a given entity or context.
-
D.
officeHoldersNumberLimit
Indicates a constraint specifying the maximum number of individuals who may simultaneously hold a particular office or position.
-
E.
servedInCabinetOf
Indicates that one person held a position as a member of the governmental cabinet led by another person.
- 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_69ca834ca1c88190a11ffb0200342fac |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc47944d1c819081f448f14d04bf9d |
completed | March 31, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69cc455d6d448190a2da2a319ac78c37 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:28 p.m.