Triple
T7369118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamid |
E169947
|
entity |
| Predicate | hasFrequencyDescription |
P18808
|
FINISHED |
| Object | common in Muslim-majority 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: common in Muslim-majority countries | Statement: [Hamid, hasFrequencyDescription, common in Muslim-majority countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrequencyDescription Context triple: [Hamid, hasFrequencyDescription, common in Muslim-majority countries]
-
A.
hasFrequencyCategory
Indicates that something is associated with a particular classification of how often it occurs or is used.
-
B.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
-
C.
hasFrequencyNote
chosen
Indicates that something is associated with a specific note describing how often it occurs or is repeated.
-
D.
hasFrequencyCoverage
Indicates that one entity provides, supports, or is applicable across a specified range or set of frequencies associated with another entity.
-
E.
isFrequently
Indicates that an action, state, or relationship occurs often or with high regularity between the related entities.
- 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_69c68a5ade988190885b7175f63b7534 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f26d6d6081909c7272a9ccae0d97 |
completed | March 27, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_69c6f02d36108190bcb34a95e6a30bd7 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:07 p.m.