Triple
T1298694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monroe, North Carolina |
E27711
|
entity |
| Predicate | populationRange |
P28398
|
FINISHED |
| Object | between 30,000 and 40,000 inhabitants |
—
|
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: between 30,000 and 40,000 inhabitants | Statement: [Monroe, North Carolina, populationRange, between 30,000 and 40,000 inhabitants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationRange Context triple: [Monroe, North Carolina, populationRange, between 30,000 and 40,000 inhabitants]
-
A.
demographicScope
Indicates the specific population group or demographic segment to which something (e.g., a policy, study, product, or service) is targeted or applicable.
-
B.
populationIncludes
Indicates that a population contains or encompasses the specified individual(s) or subgroup(s) as members or elements.
-
C.
populationClass
Indicates a categorical classification of a population based on shared characteristics, status, or demographic criteria.
-
D.
countryPopulationContext
Indicates the contextual population characteristics or statistics associated with a specific country.
-
E.
populationDemonym
Indicates the term used to refer to the people or inhabitants associated with a particular place or region.
- 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_69a496d6682881909ba658f1c1e0e2b0 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c3bb3a9c81909db2ad91defd87b6 |
completed | March 1, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69a4bee64d908190b6a9bb479959d523 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c3b9ebdc819098de4d3288201bc1 |
completed | March 1, 2026, 10:54 p.m. |
Created at: March 1, 2026, 7:51 p.m.