Triple
T154381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | World Health Day |
E3145
|
entity |
| Predicate | themeExamples |
P7707
|
FINISHED |
| Object | universal health coverage |
—
|
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: universal health coverage | Statement: [World Health Day, themeExamples, universal health coverage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: themeExamples Context triple: [World Health Day, themeExamples, universal health coverage]
-
A.
theme
Indicates the entity that is the primary participant or content affected or characterized by an action, event, or state.
-
B.
colors
Indicates that one entity assigns, describes, or provides the color or colors of another entity.
-
C.
designUse
Indicates that one entity is used as a design basis, purpose, or intended functional use for another entity.
-
D.
campaignTheme
Indicates the central idea or message that characterizes and unifies a particular campaign.
-
E.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
- 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a258e0b11c8190b7b5cf3c354c47ce |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a2565c727c8190bca9ba6ca52f216a |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a258de46888190835db2b21a093eaa |
completed | Feb. 28, 2026, 2:54 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.