Triple
T38178053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Badger, Minnesota |
E1000269
|
entity |
| Predicate | racialMakeup2010TwoOrMoreRacesPercentage |
P62280
|
FINISHED |
| Object | 0.5% |
—
|
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: 0.5% | Statement: [Badger, Minnesota, racialMakeup2010TwoOrMoreRacesPercentage, 0.5%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: racialMakeup2010TwoOrMoreRacesPercentage Context triple: [Badger, Minnesota, racialMakeup2010TwoOrMoreRacesPercentage, 0.5%]
-
A.
demographicsOtherRacesPresent2000
Indicates that, in the year 2000, the demographic data show the presence of racial groups categorized as "other races" beyond the primary race categories.
-
B.
populationTotal2010
Indicates the total number of individuals in a population as measured or recorded for the year 2010.
-
C.
racialComposition
chosen
Indicates the proportional makeup of different racial groups within a given population or entity.
-
D.
inUSCensus
Indicates that an entity is recorded or included in the United States Census.
-
E.
rankInUS2010Census
Indicates the numerical position of an entity in the ranking of occurrences within the 2010 United States Census.
- 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_69fcd867f36081908c88c55a6a1404c1 |
completed | May 7, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f47b188190b4cf4b4c748d9d03 |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 3, 2026, 4:29 p.m.