Triple
T1311112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Somerset County |
E27991
|
entity |
| Predicate | rural |
P2460
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Somerset County, rural, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rural Context triple: [Somerset County, rural, true]
-
A.
isRural
chosen
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
-
B.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
C.
spokenInRuralAreasOf
Indicates that something (typically a language, dialect, or speech variety) is used or spoken primarily in the rural areas of a specified region or country.
-
D.
isRuralCounty
Indicates that a given county is classified as rural rather than urban based on demographic, geographic, or administrative criteria.
-
E.
countryTheme
Indicates that something is associated with or represents a thematic focus on a particular country.
- 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_69a496d7d83481908f83085854e51328 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c1560f888190bdd9107b08395e0b |
completed | March 1, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69a4bee9e4a88190b22ab2ee831a23c9 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:51 p.m.