Triple
T7008790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saline County, Kansas |
E162525
|
entity |
| Predicate | hasCountyNumberInKansas |
P73560
|
FINISHED |
| Object | 85 |
—
|
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: 85 | Statement: [Saline County, Kansas, hasCountyNumberInKansas, 85]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCountyNumberInKansas Context triple: [Saline County, Kansas, hasCountyNumberInKansas, 85]
-
A.
hasCountyNumberInSouthDakota
Indicates that an entity is associated with a specific officially assigned county number within the state of South Dakota.
-
B.
hasCountyCode
Indicates that an entity is associated with a specific county identified by a standardized county code.
-
C.
hasCountyNumberInTennessee
Indicates that an entity is assigned a specific official county number within the state of Tennessee.
-
D.
numberPerCounty
Indicates the quantity or count of something associated with each individual county.
-
E.
hasCountyNumberPlateCode
Indicates that an entity (such as a vehicle or registration) bears a number plate code that corresponds to a specific county.
- 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_69c6885928148190ae31909fbb5e9849 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc38039c8190ab420d62bb11b4d9 |
completed | March 27, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c67c94819084fdcf0398606027 |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6d8c575f081908b43d95d1d99b1a4 |
completed | March 27, 2026, 7:21 p.m. |
Created at: March 27, 2026, 2:33 p.m.