Triple
T35362480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greenbush Elementary School |
E1021528
|
entity |
| Predicate | isLocatedInUrbanOrRuralArea |
P60791
|
FINISHED |
| Object | suburban area |
—
|
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: suburban area | Statement: [Greenbush Elementary School, isLocatedInUrbanOrRuralArea, suburban area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLocatedInUrbanOrRuralArea Context triple: [Greenbush Elementary School, isLocatedInUrbanOrRuralArea, suburban area]
-
A.
isRuralOrUrban
chosen
Indicates whether an entity is classified as being in a rural area or an urban area.
-
B.
isUrbanOrNearUrban
Indicates that something is located within an urban area or in close proximity to an urban area.
-
C.
locatedInUrbanizationType
Indicates that one entity is situated within, or belongs to, a specific type or category of urbanized area (e.g., city, suburb, metropolitan zone).
-
D.
isInRuralAreaOf
Indicates that one entity is located within the rural area or countryside region associated with another entity.
-
E.
hasUrbanAreaApprox
Indicates an approximate measure or estimate of the size or extent of an entity’s urban area.
- 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_69f76def44c881908a20e8008572eb44 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fbbc49da8c8190902bbb05d2477cab |
completed | May 6, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69fbb13f34b08190bbbb220ac1e6e666 |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:03 p.m.