Triple
T34824406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kono people |
E1003875
|
entity |
| Predicate | civilWarImpact |
P82029
|
FINISHED |
| Object | heavily affected by Sierra Leone Civil War |
—
|
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: heavily affected by Sierra Leone Civil War | Statement: [Kono people, civilWarImpact, heavily affected by Sierra Leone Civil War]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: civilWarImpact Context triple: [Kono people, civilWarImpact, heavily affected by Sierra Leone Civil War]
-
A.
civilWarSignificance
Indicates the importance or impact that a particular entity, event, or factor has within the context of a civil war.
-
B.
civilWarPeriod
Indicates a time span during which a civil war is occurring or in effect.
-
C.
hasCivilWarAssociation
chosen
Indicates a relationship in which an entity is connected or related to a civil war, such as through involvement, impact, or relevance.
-
D.
historicalImpact
Indicates the influence or lasting effects that an entity, event, or action has had on subsequent history or historical developments.
-
E.
従軍紛争
Indicates participation in or involvement with a military conflict or war.
- 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_69f76db717088190811b4e744610f37d |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f7886be6d8819095ec62e4f2cee858 |
completed | May 3, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69f7841440f48190b4346c08855951d2 |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4 p.m.