Triple
T30229947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juja |
E768597
|
entity |
| Predicate | primaryPopulationDrivers |
P3838
|
FINISHED |
| Object | university student population |
—
|
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: university student population | Statement: [Juja, primaryPopulationDrivers, university student population]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryPopulationDrivers Context triple: [Juja, primaryPopulationDrivers, university student population]
-
A.
urbanizationDriver
Indicates that a factor or process contributes to or causes the growth, expansion, or intensification of urban areas.
-
B.
demographicImpact
chosen
Indicates how an action, event, or condition affects the size, structure, or composition of a population.
-
C.
populationFocus
Indicates that something is primarily directed toward, concerned with, or designed for a particular population or demographic group.
-
D.
population
Indicates the total number of individuals living in or present within a specified area or group.
-
E.
populationOutcome
Indicates the resulting state, condition, or effect experienced by a population as a consequence of a specified exposure, intervention, or circumstance.
- 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_69f2248108208190be60bf1af343ce70 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a01d9ca07b88190b4ad70b6336447b6 |
completed | May 11, 2026, 1:29 p.m. |
| PD | Predicate disambiguation | batch_6a01d807c3048190b79f0b6b933dd3b7 |
completed | May 11, 2026, 1:22 p.m. |
Created at: April 29, 2026, 7:36 p.m.