Triple
T8250413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elkton, Kentucky |
E192942
|
entity |
| Predicate | hasPopulationEstimateYear |
P5555
|
FINISHED |
| Object | 2020 |
—
|
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: 2020 | Statement: [Elkton, Kentucky, hasPopulationEstimateYear, 2020]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopulationEstimateYear Context triple: [Elkton, Kentucky, hasPopulationEstimateYear, 2020]
-
A.
hasPopulationAsOf
chosen
Indicates that a population count is associated with a specific point or date in time when that population figure was valid or recorded.
-
B.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
-
C.
populationCensusYear
Indicates the specific year in which an official population census was conducted or recorded for an entity.
-
D.
approximatePopulationTrend
Indicates an estimated or generalized pattern of how a population changes over time (e.g., increasing, decreasing, or stable) rather than an exact count.
-
E.
basedOnCensusYear
Indicates that something (such as data, a decision, or a classification) is derived from or determined using information from a specific census year.
- 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_69ca82de7b8c81908d8106f8a53cff9b |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb78c935408190b9196a849a8d3a3e |
completed | March 31, 2026, 7:33 a.m. |
| PD | Predicate disambiguation | batch_69cb36b6d5548190b665a6cce14c69f7 |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:48 p.m.