Triple
T6891069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spanish language in Ecuador |
E159045
|
entity |
| Predicate | dominantInUrbanAreas |
P40494
|
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: [Spanish language in Ecuador, dominantInUrbanAreas, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dominantInUrbanAreas Context triple: [Spanish language in Ecuador, dominantInUrbanAreas, yes]
-
A.
withinUrbanArea
Indicates that one entity is located inside the spatial boundaries of an urban area associated with another entity.
-
B.
statusInUrbanAreas
chosen
Indicates the condition, prevalence, or situation of something specifically within urban areas.
-
C.
advantageOverUrbanAreas
Indicates that one entity (typically a rural or non-urban area) possesses a comparative benefit or favorable condition relative to urban areas.
-
D.
isUrbanizing
Indicates a process in which an area or population becomes more urban in character, typically through increased development, infrastructure, and concentration of people and activities.
-
E.
isUrbanDistrict
Indicates that a given district is classified as an urban administrative or residential area rather than a rural one.
- 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_69c6883568c8819081db6407e892cccc |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d92d45f08190a730b3842c95b521 |
completed | March 27, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b53e9881909ec298daa9f1913b |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:24 p.m.