Triple
T4956569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sherborn, Massachusetts |
E111294
|
entity |
| Predicate | isLargelyRural |
P47416
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Sherborn, Massachusetts, isLargelyRural, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLargelyRural Context triple: [Sherborn, Massachusetts, isLargelyRural, yes]
-
A.
isPredominantlyRural
chosen
Indicates that a place or region is characterized mainly by rural features, such as low population density and extensive non-urban land use.
-
B.
isRural
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
-
C.
isRuralCounty
Indicates that a given county is classified as rural rather than urban based on demographic, geographic, or administrative criteria.
-
D.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
E.
isInRuralAreaOf
Indicates that one entity is located within the rural area or countryside region associated with another entity.
- 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_69bd4418390c8190b7e9766a2512ce55 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd71d6ba9c8190932f8d81240c3f56 |
completed | March 20, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69bd6c3beb008190852fd6150813b252 |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:32 p.m.