Triple
T6207466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Red Crescent Societies |
E138783
|
entity |
| Predicate | typicalCountryContext |
P3248
|
FINISHED |
| Object | predominantly Muslim countries |
—
|
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: predominantly Muslim countries | Statement: [National Red Crescent Societies, typicalCountryContext, predominantly Muslim countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCountryContext Context triple: [National Red Crescent Societies, typicalCountryContext, predominantly Muslim countries]
-
A.
commonInCountry
Indicates that something occurs frequently or is widespread within a specified country.
-
B.
typicalCountryIncluded
chosen
Indicates that a country is commonly or characteristically included within a given grouping, context, or set.
-
C.
hasCountryContext
Indicates that something is associated with, interpreted within, or relevant to a specific country or national context.
-
D.
mainCountry
Indicates that one country is the primary or most significant country associated with a given entity or context.
-
E.
countryFeatured
Indicates that a particular country is highlighted or given special prominence in a given context or presentation.
- 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_69c008ada364819096c9e92c74d639b5 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06270b8d08190a81bc03c8175b989 |
completed | March 22, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69c055fdea3c81908f5d910f0d36234a |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:20 p.m.