Triple
T23342277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sutton |
E591765
|
entity |
| Predicate | isEspeciallyCommonIn |
P22713
|
FINISHED |
| Object | English rural areas |
—
|
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: English rural areas | Statement: [Sutton, isEspeciallyCommonIn, English rural areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isEspeciallyCommonIn Context triple: [Sutton, isEspeciallyCommonIn, English rural areas]
-
A.
moreCommonIn
Indicates that something occurs with greater frequency or prevalence in one group, context, or location than in another.
-
B.
prevalentIn
chosen
Indicates that something occurs frequently or is commonly found within a particular context, group, or environment.
-
C.
isLessCommonThan
Indicates that one item occurs, appears, or is observed less frequently than another item.
-
D.
isLessCommonSince
Indicates that the frequency or prevalence of one entity has decreased relative to another entity or to its own past occurrence from a specified point in time.
-
E.
countryOrRegionOfPrevalence
Indicates the country or geographic region where something (such as a condition, practice, or phenomenon) is most commonly found or occurs most frequently.
- 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_69e25d20e3d08190bcede87673cafb25 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f198334dec819081c432b434eb3ba5 |
completed | April 29, 2026, 5:33 a.m. |
| PD | Predicate disambiguation | batch_69effcfd8d288190937a887fe6023c11 |
completed | April 28, 2026, 12:19 a.m. |
Created at: April 17, 2026, 5:18 p.m.