Triple
T432982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howard County, Iowa |
E9752
|
entity |
| Predicate | hasCountySeatFunction |
P13896
|
FINISHED |
| Object | local government administration |
—
|
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: local government administration | Statement: [Howard County, Iowa, hasCountySeatFunction, local government administration]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCountySeatFunction Context triple: [Howard County, Iowa, hasCountySeatFunction, local government administration]
-
A.
countySeat
Indicates that one place serves as the administrative center or capital of a county.
-
B.
hasMetropolitanCounty
Indicates that an entity is associated with, located within, or administered by a specific metropolitan county.
-
C.
mayorSeatOf
Indicates that a particular mayor holds office as the chief elected official of a specified city or municipality.
-
D.
hasCountyCode
Indicates that an entity is associated with a specific county identified by a standardized county code.
-
E.
countyName
Indicates the specific name assigned to a county in which an entity is located or with which it is associated.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ef084840819080653004b674cba8 |
completed | Feb. 28, 2026, 1:35 p.m. |
| PD | Predicate disambiguation | batch_69a2edda55e88190b7c17ba94d7df1ce |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eeb93584819082f23eff13e17c4f |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.