Triple
T28690538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bassirou Diomaye Faye |
E729268
|
entity |
| Predicate | sectorReformFocus |
P98268
|
FINISHED |
| Object | tax system |
—
|
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: tax system | Statement: [Bassirou Diomaye Faye, sectorReformFocus, tax system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sectorReformFocus Context triple: [Bassirou Diomaye Faye, sectorReformFocus, tax system]
-
A.
sectorReformed
Indicates that a particular sector has undergone significant changes or restructuring, typically through reforms or policy interventions.
-
B.
sectoralCoverage
Indicates the specific sectors, industries, or domains to which something (such as a policy, agreement, or dataset) applies or extends.
-
C.
typeOfReforms
chosen
Indicates the specific kinds or categories of reforms associated with an entity or situation.
-
D.
sectorBenefited
Indicates that a particular sector gains advantage, support, or positive impact from a given action, policy, resource, or entity.
-
E.
sectoralEngagement
Indicates engagement or involvement between entities within a specific sector or industry context.
- 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_69f043e60b6c8190ac2cd042e77fe6e9 |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f658ee40088190b71e1219407690d0 |
completed | May 2, 2026, 8:05 p.m. |
| PD | Predicate disambiguation | batch_69f65760fd3081908ffe014a5e2bf069 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 28, 2026, 5:35 a.m.