Triple
T34660606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hazzard County, Georgia |
E890095
|
entity |
| Predicate | hasFictionalGarage |
P30711
|
FINISHED |
| Object | Cooter’s Garage |
—
|
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: Cooter’s Garage | Statement: [Hazzard County, Georgia, hasFictionalGarage, Cooter’s Garage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalGarage Context triple: [Hazzard County, Georgia, hasFictionalGarage, Cooter’s Garage]
-
A.
hasGarage
chosen
Indicates that one entity possesses or includes a garage associated with it.
-
B.
hasFictionalGasStation
Indicates that one entity includes, features, or is associated with a fictional gas station.
-
C.
hasFictionalDriver
Indicates that an entity (such as a vehicle or object) is associated with a driver who is a fictional or imaginary character.
-
D.
hasFictionalVehicle
Indicates that one entity possesses, controls, or is associated with a vehicle that exists only in a fictional or imaginary context.
-
E.
hasFictionalProprietor
Indicates that something is owned, managed, or run by a fictional character or entity within a narrative context.
- 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_69ff7eb7189c81909a8f73fbc4c48e02 |
completed | May 9, 2026, 6:36 p.m. |
| PD | Predicate disambiguation | batch_69ff7e54e11081908fb5ce10c5aa7b53 |
completed | May 9, 2026, 6:35 p.m. |
Created at: May 1, 2026, 2:04 a.m.