Triple
T34660616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hazzard County, Georgia |
E890095
|
entity |
| Predicate | hasFictionalNeighboringCounty |
P200323
|
FINISHED |
| Object | Chickasaw County |
—
|
NE NERFINISHED |
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: Chickasaw County | Statement: [Hazzard County, Georgia, hasFictionalNeighboringCounty, Chickasaw County]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalNeighboringCounty Context triple: [Hazzard County, Georgia, hasFictionalNeighboringCounty, Chickasaw County]
-
A.
hasFictionalCounty
Indicates that one entity includes, is set in, or is associated with a county that is fictional rather than real.
-
B.
hasFictionalCountySeatRole
Indicates that an entity serves in the role of county seat within a fictional or imaginary administrative setting.
-
C.
hasNearbyCounty
Indicates that one county is geographically close to or directly adjacent to another county.
-
D.
hasFictionalNearbyTown
Indicates that an entity is associated with a fictional town located in its vicinity or surrounding area.
-
E.
hasNearbyCountyBorder
Indicates that the borders of two counties are geographically close to each other, though not necessarily directly adjacent.
- 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_69f349d906bc8190b2efd9eff237d94b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff80d9a1d88190a95b1488acd6e2e5 |
completed | May 9, 2026, 6:45 p.m. |
| PD | Predicate disambiguation | batch_69ff802ae2dc819093a3cda42b63dcbd |
completed | May 9, 2026, 6:42 p.m. |
| PDg | Predicate description generation | batch_69ff80d8ff208190b9e95d077fd99f78 |
completed | May 9, 2026, 6:45 p.m. |
Created at: May 1, 2026, 2:04 a.m.