Triple
T19486101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rēzekne, Latvian SSR, Soviet Union |
E487514
|
entity |
| Predicate | hadPopulationTrend |
P31774
|
FINISHED |
| Object | urbanization during Soviet period |
—
|
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: urbanization during Soviet period | Statement: [Rēzekne, Latvian SSR, Soviet Union, hadPopulationTrend, urbanization during Soviet period]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadPopulationTrend Context triple: [Rēzekne, Latvian SSR, Soviet Union, hadPopulationTrend, urbanization during Soviet period]
-
A.
approximatePopulationTrend
chosen
Indicates an estimated or generalized pattern of how a population changes over time (e.g., increasing, decreasing, or stable) rather than an exact count.
-
B.
populationIncrease
Indicates that the number of individuals in a population has grown over a specified period of time.
-
C.
hadPopulationFrom
Indicates that an entity had a specified population value during a particular time period starting from a given date.
-
D.
hadPopulationType
Indicates that an entity possessed a particular classification or type of population during a given time or context.
-
E.
demographicImpact
Indicates how an action, event, or condition affects the size, structure, or composition of a population.
- 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_69d8e8d924388190b847cb15bb3d0aff |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6343f46e88190b7ba65c210285bee |
completed | April 20, 2026, 2:12 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7883308190b73912a71a35a835 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:39 p.m.