Triple
T33090528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Go Red for Women |
E846767
|
entity |
| Predicate | healthTopic |
P109970
|
FINISHED |
| Object | cardiovascular health |
—
|
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: cardiovascular health | Statement: [Go Red for Women, healthTopic, cardiovascular health]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: healthTopic Context triple: [Go Red for Women, healthTopic, cardiovascular health]
-
A.
healthTheme
chosen
Indicates that the subject is associated with, focuses on, or is characterized by a particular health-related topic or theme.
-
B.
healthAdvisory
Indicates that one entity issues or is associated with guidance, warnings, or recommendations about health-related risks, conditions, or behaviors affecting another entity or population.
-
C.
healthAssociation
Indicates a relationship where one factor, condition, or entity is linked to an effect, outcome, or status related to health.
-
D.
healthRationale
Indicates the reasoning or justification behind an assessment, decision, or action related to health.
-
E.
medicalBackground
Indicates that an entity has a history of prior medical conditions, treatments, or health-related experiences relevant to its current state or context.
- 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_69f3495590dc8190aa04f3dec74ce976 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6e02ba6b881908dfafc52d3b75f1c |
completed | May 3, 2026, 5:42 a.m. |
| PD | Predicate disambiguation | batch_69f6de09c2f481909f8b2545d3208c9f |
completed | May 3, 2026, 5:32 a.m. |
Created at: May 1, 2026, 1:26 a.m.