Triple
T19305732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hickman County, Kentucky |
E482822
|
entity |
| Predicate | hasCountyNumberPlatePrefix |
P68833
|
FINISHED |
| Object | 60 |
—
|
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: 60 | Statement: [Hickman County, Kentucky, hasCountyNumberPlatePrefix, 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCountyNumberPlatePrefix Context triple: [Hickman County, Kentucky, hasCountyNumberPlatePrefix, 60]
-
A.
hasCountyNumberPlateCode
Indicates that an entity (such as a vehicle or registration) bears a number plate code that corresponds to a specific county.
-
B.
hasCountyCode
Indicates that an entity is associated with a specific county identified by a standardized county code.
-
C.
hasCountyCodeType
Indicates that an entity is associated with a specific type or classification of county code.
-
D.
hasPostalCodePrefix
Indicates that a location’s postal code begins with a specified sequence of characters.
-
E.
hasVehicleLicensePrefix
chosen
Indicates that one entity has, uses, or is associated with a specific vehicle license plate prefix represented by the other 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_69d8e8d04d5c8190baa816986f2b1d1e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e604c84fe08190869463bdd0324160 |
completed | April 20, 2026, 10:49 a.m. |
| PD | Predicate disambiguation | batch_69e4dd0bc7508190a6f9d56bd4c3404f |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:31 p.m.