Triple
T36439180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zimbabwean general election, 2023 |
E897678
|
entity |
| Predicate | reportedIssues |
P21734
|
FINISHED |
| Object | concerns over electoral fairness |
—
|
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: concerns over electoral fairness | Statement: [Zimbabwean general election, 2023, reportedIssues, concerns over electoral fairness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reportedIssues Context triple: [Zimbabwean general election, 2023, reportedIssues, concerns over electoral fairness]
-
A.
knownIssue
chosen
Indicates that the subject has an issue or problem that is already identified, recognized, or documented.
-
B.
reportedAs
Indicates that one entity has been formally reported or flagged to another entity, typically as problematic, suspicious, or noteworthy.
-
C.
raisesIssue
Indicates that one entity brings up, reports, or formally submits a concern, problem, or topic for attention to another entity or system.
-
D.
issuesMark
Indicates that one entity assigns or gives a mark, grade, or score to another entity.
-
E.
conditionIssues
Indicates that one entity has problems, defects, or concerns related to the state or condition of another entity.
- 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_69f76e56636481908eda808ab0273401 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7cec454a88190a9f3bbee2b856636 |
completed | May 3, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f7c8977c288190997a892ec5f756ed |
completed | May 3, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:10 p.m.