Triple
T1036025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greene County, Pennsylvania |
E22363
|
entity |
| Predicate | hasAdministrativeType |
P23924
|
FINISHED |
| Object | county of Pennsylvania |
—
|
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: county of Pennsylvania | Statement: [Greene County, Pennsylvania, hasAdministrativeType, county of Pennsylvania]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAdministrativeType Context triple: [Greene County, Pennsylvania, hasAdministrativeType, county of Pennsylvania]
-
A.
hasPublicAuthorityType
Indicates that an entity possesses a specific type or category of public authority status or role.
-
B.
hasAdministrativeUnit
Indicates that one entity possesses, contains, or is associated with another entity that functions as its administrative subdivision or governing unit.
-
C.
hasAdministrativeArea
Indicates that one entity serves as the governing or jurisdictional area responsible for administering another entity.
-
D.
hasTypeOfManagement
Indicates that an entity is associated with or operates under a specified form or style of management.
-
E.
administeredAs
Indicates that one entity is given or applied to another entity as a treatment, dose, or intervention.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8d669448190955507e2e4975b9f |
completed | March 1, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69a4b728ad3481909cf1430349cb9bba |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b8d5076481908640a0d873efdf14 |
completed | March 1, 2026, 10:08 p.m. |
Created at: March 1, 2026, 7:41 p.m.