Triple
T22080172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avonlea |
E545627
|
entity |
| Predicate | fictionalRegionType |
P71480
|
FINISHED |
| Object | rural community |
—
|
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: rural community | Statement: [Avonlea, fictionalRegionType, rural community]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalRegionType Context triple: [Avonlea, fictionalRegionType, rural community]
-
A.
fictionalGeographicRegion
Indicates that a geographic region exists only in fiction or imagination rather than in the real world.
-
B.
fictionalSettingRegion
Indicates that a fictional setting is located within or associated with a specific geographic or administrative region.
-
C.
fictionalPlaceType
chosen
Indicates that a place is a fictional location and specifies what type or category of fictional place it is.
-
D.
geographicalRegionType
Indicates the specific kind or category of geographical region that an entity belongs to (e.g., continent, country, province, or city).
-
E.
regionalType
Indicates the classification of a region according to its designated type or category within a broader geographic or administrative system.
- 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_69e11e3523488190badd54b5d580c00d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f128b56d5c8190aa825fe02e3ad917 |
completed | April 28, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69e6f64a6a70819089d1a6c3a2384861 |
completed | April 21, 2026, 4 a.m. |
Created at: April 16, 2026, 8:28 p.m.