Triple
T14195569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Sierra Leone |
E351825
|
entity |
| Predicate | alsoAttracts |
P30111
|
FINISHED |
| Object | students from other West African countries |
—
|
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: students from other West African countries | Statement: [University of Sierra Leone, alsoAttracts, students from other West African countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alsoAttracts Context triple: [University of Sierra Leone, alsoAttracts, students from other West African countries]
-
A.
attracts
Indicates that one entity exerts a force or influence that draws another entity toward it.
-
B.
attractsParticipantsFrom
chosen
Indicates that an event, activity, or organization draws or recruits participants originating from a specified place, group, or source.
-
C.
alsoIn
Indicates that an entity participates in or belongs to an additional context, group, or location alongside another already specified one.
-
D.
attractsAudience
Indicates that an entity draws the interest or attention of people who choose to watch, listen to, or engage with it.
-
E.
relatedAttraction
Indicates that one attraction is associated with or connected to another attraction in some relevant way.
- 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_69d827894ac0819097803e57f3227b23 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61e1fbd48190a4864fa4443f8f29 |
completed | April 14, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69de05baed64819096590e5618a3a8ed |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:04 a.m.