Triple
T34660600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hazzard County, Georgia |
E890095
|
entity |
| Predicate | hasFictionalOfficialRole |
P58964
|
FINISHED |
| Object | county commissioner |
—
|
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 commissioner | Statement: [Hazzard County, Georgia, hasFictionalOfficialRole, county commissioner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalOfficialRole Context triple: [Hazzard County, Georgia, hasFictionalOfficialRole, county commissioner]
-
A.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
B.
hasFictionalLeader
Indicates that an entity is led or governed by a leader who is a fictional character rather than a real person.
-
C.
hasFictionalSpokesperson
Indicates that an entity is represented or promoted by a spokesperson who is a fictional or imaginary character.
-
D.
hasInUniverseRole
chosen
Indicates that an entity holds or performs a specific role or function within a particular fictional or defined universe.
-
E.
worksWithFictionalCharacter
Indicates that one entity collaborates or interacts in a work-related context with another entity that is a fictional character.
- 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_69f349d906bc8190b2efd9eff237d94b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff7c96e6dc8190b89554480ebcea39 |
completed | May 9, 2026, 6:27 p.m. |
| PD | Predicate disambiguation | batch_69ff7c2381748190ad9a2176e0e478cd |
completed | May 9, 2026, 6:25 p.m. |
Created at: May 1, 2026, 2:04 a.m.