Triple
T8198230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R-y-a-n |
E191489
|
entity |
| Predicate | hasNameUsageRegion |
P15483
|
FINISHED |
| Object | English-speaking 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: English-speaking countries | Statement: [R-y-a-n, hasNameUsageRegion, English-speaking countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNameUsageRegion Context triple: [R-y-a-n, hasNameUsageRegion, English-speaking countries]
-
A.
hasRegionalName
Indicates that an entity is known by a specific name or designation within a particular region or locality.
-
B.
hasGivenNameUsage
Indicates that an entity is associated with a particular way or context in which its given name is used.
-
C.
regionNameUsedFor
Indicates that a particular region name is used to refer to or designate a specific region or area.
-
D.
hasTypicalUsageRegion
chosen
Indicates that something is most commonly or characteristically used within a particular geographic region.
-
E.
usedInRegion
Indicates that something is utilized or applied within a specific geographic or administrative region.
- 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_69ca82c6e9548190a4c5ca14516e4417 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb5c2341f881908be59c378896e5bc |
completed | March 31, 2026, 5:31 a.m. |
| PD | Predicate disambiguation | batch_69cb36aac86081909b83636e352e0ced |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:42 p.m.