Triple
T23361349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayo-Danay |
E593191
|
entity |
| Predicate | hasRuralUrbanProfile |
P24917
|
FINISHED |
| Object | mostly rural |
—
|
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: mostly rural | Statement: [Mayo-Danay, hasRuralUrbanProfile, mostly rural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRuralUrbanProfile Context triple: [Mayo-Danay, hasRuralUrbanProfile, mostly rural]
-
A.
isRuralOrUrban
Indicates whether an entity is classified as being in a rural area or an urban area.
-
B.
hasUrbanPopulationIn
Indicates that an entity has a specified urban population within a particular geographic area or administrative unit.
-
C.
hasUrbanClassification
Indicates that an entity is assigned a specific urban status or category within a defined classification system.
-
D.
hasUrbanRuralMix
chosen
Indicates that something exhibits a combination or blend of both urban and rural characteristics or components.
-
E.
isRuralCity
Indicates that a city is characterized by rural features or is located within a predominantly rural 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_69e25d24d2a4819092e6ede74c2a918d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a0a8669c819098b88ae6712e3f88 |
completed | April 29, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69f061c7aaa48190a58ce93f87155ffc |
completed | April 28, 2026, 7:29 a.m. |
Created at: April 17, 2026, 5:30 p.m.