Triple
T2158367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EMRO |
E47943
|
entity |
| Predicate | numberOfRegionalOfficesInWHO |
P36326
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [EMRO, numberOfRegionalOfficesInWHO, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRegionalOfficesInWHO Context triple: [EMRO, numberOfRegionalOfficesInWHO, 6]
-
A.
numberOfCountryOffices
Indicates the total count of offices or branches that an organization maintains across different countries.
-
B.
hasInternationalOrganizationOffice
Indicates that an international organization maintains an official office or physical presence at a given location.
-
C.
numberOfOffices
Indicates the total count of offices associated with a given entity.
-
D.
coordinatesInternationalHealth
Indicates organizing and aligning health-related activities, policies, or responses across multiple countries or international bodies.
-
E.
UNRegion
Indicates that an entity belongs to, or is associated with, a specific United Nations–defined geographic region.
- F. None of above. chosen
Provenance (4 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_69a88a1d1fd8819088b34990d69a712f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe68fe0c8190beb5db003738a6e5 |
completed | March 7, 2026, 5:58 a.m. |
| PD | Predicate disambiguation | batch_69abbd9a60648190b20b116be5c7ad98 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbe4252688190944491a450383450 |
completed | March 7, 2026, 5:57 a.m. |
Created at: March 4, 2026, 7:44 p.m.