Triple
T32734704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | INTERPOL Secretariat General |
E837042
|
entity |
| Predicate | numberOfMemberCountriesServed |
P3809
|
FINISHED |
| Object | 195 |
—
|
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: 195 | Statement: [INTERPOL Secretariat General, numberOfMemberCountriesServed, 195]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMemberCountriesServed Context triple: [INTERPOL Secretariat General, numberOfMemberCountriesServed, 195]
-
A.
numberOfRegionalMembers
Indicates the quantity of members associated with or belonging to a specific region within a given context.
-
B.
numberOfMemberStates
Indicates the total count of member states associated with a given entity or organization.
-
C.
hasNumberOfCountries
chosen
Indicates the relationship that specifies how many countries are associated with or contained within a given entity.
-
D.
hasNonRegionalMemberCountries
Indicates that an organization includes member countries that are not part of the primary geographic region with which the organization is associated.
-
E.
hasCountryServed
Indicates that a person or organization has provided service to, or performed duties on behalf of, 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_69f34935fb048190ad4967420581f835 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a01d077dcec8190b05b24b5de313b15 |
completed | May 11, 2026, 12:50 p.m. |
| PD | Predicate disambiguation | batch_6a01cea0e37881909cb6888518c6d12f |
completed | May 11, 2026, 12:42 p.m. |
Created at: May 1, 2026, 1:12 a.m.