Triple
T38178052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Badger, Minnesota |
E1000269
|
entity |
| Predicate | racialMakeup2010AsianPercentage |
P62280
|
FINISHED |
| Object | 0.3% |
—
|
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: 0.3% | Statement: [Badger, Minnesota, racialMakeup2010AsianPercentage, 0.3%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: racialMakeup2010AsianPercentage Context triple: [Badger, Minnesota, racialMakeup2010AsianPercentage, 0.3%]
-
A.
rankInUS2010Census
Indicates the numerical position of an entity in the ranking of occurrences within the 2010 United States Census.
-
B.
populationTotal2010
Indicates the total number of individuals in a population as measured or recorded for the year 2010.
-
C.
ethnicGroupCountApprox
Indicates an approximate count of distinct ethnic groups associated with or present in a given entity.
-
D.
populationEthnicGroup
Indicates that a specified ethnic group constitutes part or all of the population of a given place or community.
-
E.
racialComposition
chosen
Indicates the proportional makeup of different racial groups within a given population or entity.
- 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_69f76daaace48190a38cee37f8ce343f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fccdd496048190bca801a8a9eecb62 |
completed | May 7, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69fcccee6240819084680887731ff64b |
completed | May 7, 2026, 5:33 p.m. |
Created at: May 3, 2026, 4:29 p.m.