Triple
T10719836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sparta, Mississippi |
E252786
|
entity |
| Predicate | hasFictionalRegionType |
P71480
|
FINISHED |
| Object | small town |
—
|
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: small town | Statement: [Sparta, Mississippi, hasFictionalRegionType, small town]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalRegionType Context triple: [Sparta, Mississippi, hasFictionalRegionType, small town]
-
A.
hasFictionalLocation
Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
-
B.
belongsToFictionalContinent
Indicates that something is located on, associated with, or a part of a specific fictional continent within an imagined world.
-
C.
hasFictionalEstablishmentType
Indicates that an establishment is associated with a particular type or category of fictional setting or institution.
-
D.
fictionalPlaceType
chosen
Indicates that a place is a fictional location and specifies what type or category of fictional place it is.
-
E.
hasBranchInFictionalLocation
Indicates that an organization maintains a branch, office, or presence within a fictional or imaginary location.
- 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_69d6aa5d8be481909a43218b2bfdbe95 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6ff3722ec8190b2d78a5630bf6efc |
completed | April 9, 2026, 1:21 a.m. |
| PD | Predicate disambiguation | batch_69d6f30455888190b77f476b8418eaee |
completed | April 9, 2026, 12:29 a.m. |
Created at: April 8, 2026, 9:13 p.m.