Triple
T14268231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mansa |
E353705
|
entity |
| Predicate | influencedTitleSystems |
P113503
|
FINISHED |
| Object | later West African kingdoms |
—
|
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: later West African kingdoms | Statement: [Mansa, influencedTitleSystems, later West African kingdoms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencedTitleSystems Context triple: [Mansa, influencedTitleSystems, later West African kingdoms]
-
A.
titleSystem
Indicates that a system assigns or holds a formal title or designation for an entity.
-
B.
influencedNameOf
Indicates that one entity has affected or shaped the naming or choice of name of another entity.
-
C.
hadTitleSystem
Indicates that an entity possessed or was associated with a particular title within a specified system of titles.
-
D.
typeOfInfluence
Indicates the specific nature or category of influence that one entity exerts on another.
-
E.
influencedTechnology
Indicates that one entity has had a causal or shaping impact on the development, design, or use of a technological entity.
- F. None of above. chosen
Provenance (4 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_69d8278d25148190abf1a8c8f5f533ad |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6358c2288190ac1fd26e688a605d |
completed | April 14, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69de2a7d586c8190846ff242bbf5ac53 |
completed | April 14, 2026, 11:52 a.m. |
| PDg | Predicate description generation | batch_69de2e07d1f88190bdcd20967e484718 |
completed | April 14, 2026, 12:07 p.m. |
Created at: April 10, 2026, 1:10 a.m.