Triple
T26617312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clanton, Mississippi |
E668095
|
entity |
| Predicate | hasFictionalCountySeatStatus |
P55203
|
FINISHED |
| Object | Ford 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: Ford County | Statement: [Clanton, Mississippi, hasFictionalCountySeatStatus, Ford County]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalCountySeatStatus Context triple: [Clanton, Mississippi, hasFictionalCountySeatStatus, Ford County]
-
A.
hasFictionalCountySeatRole
Indicates that an entity serves in the role of county seat within a fictional or imaginary administrative setting.
-
B.
hasFictionalCounty
chosen
Indicates that one entity includes, is set in, or is associated with a county that is fictional rather than real.
-
C.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
D.
hasCountySeatFeature
Indicates that a county has a specific feature serving as its official county seat (administrative center).
-
E.
isInCountySeatOf
Indicates that one entity is located within the town or city that serves as the administrative center (county seat) of a specified county.
- 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_69ee9cfe16088190a3dddd68e3c7b1ea |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f74c70fd248190a9d5543afcb08211 |
completed | May 3, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69f7478e3b548190a51d5d436e2bb036 |
completed | May 3, 2026, 1:03 p.m. |
Created at: April 27, 2026, 2:19 a.m.