Triple
T10922658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mauritius Time |
E257984
|
entity |
| Predicate | appliesUniformlyAcrossCountry |
P4880
|
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: [Mauritius Time, appliesUniformlyAcrossCountry, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesUniformlyAcrossCountry Context triple: [Mauritius Time, appliesUniformlyAcrossCountry, yes]
-
A.
usedUniformlyAcrossCountry
chosen
Indicates that something is applied or practiced in the same way throughout the entire country without regional variation.
-
B.
appliesToPersonNationality
Indicates that something is relevant or applicable specifically to a person’s nationality.
-
C.
appliesAcross
Indicates that a condition, rule, or property holds uniformly over multiple items, cases, or contexts.
-
D.
allocatesToCountry
Indicates that a resource, amount, or responsibility is assigned or distributed to a specific country.
-
E.
commonInCountry
Indicates that something occurs frequently or is widespread within a specified country.
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7708d1fb88190bb33b72d4330ce11 |
completed | April 9, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_69d72e799f808190b6ab64fc7586a303 |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:22 p.m.