Triple
T25809764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Restaurant Brands International |
E650073
|
entity |
| Predicate | hasNumberOfCountriesOfOperation |
P93697
|
FINISHED |
| Object | 100+ 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: 100+ countries | Statement: [Restaurant Brands International, hasNumberOfCountriesOfOperation, 100+ countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfCountriesOfOperation Context triple: [Restaurant Brands International, hasNumberOfCountriesOfOperation, 100+ countries]
-
A.
numberOfCountriesWithOperations
chosen
Indicates the count of distinct countries in which an entity conducts operations or activities.
-
B.
hasNumberOfContinentsWithOperations
Indicates the count of distinct continents in which an entity conducts operations.
-
C.
operatesInCountries
Indicates that an entity conducts its activities or business within the specified countries.
-
D.
hasNumberOfCountries
Indicates the relationship that specifies how many countries are associated with or contained within a given entity.
-
E.
operatedByCountry
Indicates that an entity (such as an organization, facility, or service) is run, managed, or controlled by a specific country or its government.
- 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_69e7ab35d264819095367f7e80c983ff |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f6a8df16a88190a23820e64a3b1f92 |
completed | May 3, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69f6a751d5e48190a77dcecbe7ef9f0b |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 22, 2026, 7:08 a.m.