Triple
T15402041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake County, Minnesota |
E368343
|
entity |
| Predicate | FIPS county code |
P227
|
FINISHED |
| Object | 075 |
—
|
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: 075 | Statement: [Lake County, Minnesota, FIPS county code, 075]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FIPS county code Context triple: [Lake County, Minnesota, FIPS county code, 075]
-
A.
FIPSCode
chosen
Indicates the standardized Federal Information Processing Standards (FIPS) code assigned to identify a specific geographic or administrative entity.
-
B.
hasCountyCode
Indicates that an entity is associated with a specific county identified by a standardized county code.
-
C.
hasCountyNumberPlateCode
Indicates that an entity (such as a vehicle or registration) bears a number plate code that corresponds to a specific county.
-
D.
isCornerCountyOf
Indicates that a county lies at or near the corner where two or more larger administrative regions (such as states or districts) meet.
-
E.
FIPSCountryCode
Indicates that an entity is associated with a specific country as identified by its FIPS (Federal Information Processing Standards) country code.
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e8ea0ac8190a5c68b1951ad3db1 |
completed | April 16, 2026, 1:42 a.m. |
| PD | Predicate disambiguation | batch_69ded27b8cac8190bfa77698d53c5d1c |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:19 a.m.