Triple
T2744186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BGR |
E60825
|
entity |
| Predicate | identifiesCountryIn |
P41324
|
FINISHED |
| Object | machine-readable contexts |
—
|
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: machine-readable contexts | Statement: [BGR, identifiesCountryIn, machine-readable contexts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: identifiesCountryIn Context triple: [BGR, identifiesCountryIn, machine-readable contexts]
-
A.
associatedCountry
Indicates that there is a relevant connection or linkage between an entity and a specific country, such as origin, operation, or affiliation.
-
B.
countryLabel
Indicates the name or label of the country associated with an entity.
-
C.
hasCountry
Indicates that one entity possesses, is associated with, or is located within a specific country.
-
D.
countryOrRegion
Indicates that one entity is a country or geographic region associated with another entity (such as its location, jurisdiction, or area of relevance).
-
E.
countryFeatured
Indicates that a particular country is highlighted or given special prominence in a given context or presentation.
- F. None of above. chosen
Provenance (4 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_69ab4b79846081909096725374d65ce9 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb32ef74819096ae399d16d4f31d |
completed | March 7, 2026, 8 a.m. |
| PD | Predicate disambiguation | batch_69abd829f1e88190aab1d54f87c69714 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd8dd05c48190b4f90031642c4091 |
completed | March 7, 2026, 7:50 a.m. |
Created at: March 6, 2026, 9:56 p.m.