Triple
T3951983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Timbuktu Region |
E84883
|
entity |
| Predicate | hasSecuritySituation |
P30019
|
FINISHED |
| Object | affected by armed conflict |
—
|
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: affected by armed conflict | Statement: [Timbuktu Region, hasSecuritySituation, affected by armed conflict]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSecuritySituation Context triple: [Timbuktu Region, hasSecuritySituation, affected by armed conflict]
-
A.
hasSecurityPresence
Indicates that some form of security personnel, system, or measures are present at or associated with an entity or location.
-
B.
hasSecurityDimension
Indicates that something possesses or is associated with a particular aspect or dimension of security.
-
C.
hasSecurityEvent
chosen
Indicates that a security-related incident or event is associated with, or has occurred for, a given entity.
-
D.
hasSecurityNotion
Indicates that one entity possesses, defines, or is associated with a particular concept or notion of security in relation to another entity or context.
-
E.
hasSecurityTeam
Indicates that an entity is supported or protected by a designated security team responsible for its safety or security operations.
- 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_69aed934fbfc8190847068e4546de963 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaa5afdc8190b709af2473d75d02 |
completed | March 9, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69aef8ed04e4819096bced8971cd888d |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:30 p.m.