Triple
T30302535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tea |
E770687
|
entity |
| Predicate | hasHealthAssociation |
P123837
|
FINISHED |
| Object | May support 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: May support cardiovascular health | Statement: [Tea, hasHealthAssociation, May support cardiovascular health]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHealthAssociation Context triple: [Tea, hasHealthAssociation, May support cardiovascular health]
-
A.
healthAssociation
chosen
Indicates a relationship where one factor, condition, or entity is linked to an effect, outcome, or status related to health.
-
B.
hasHealthArea
Indicates that an entity is associated with, or falls within the scope of, a particular health-related domain or area of concern.
-
C.
hasHealthCareInstitutionType
Indicates that an entity is classified as a specific type or category of healthcare institution.
-
D.
hasAffiliatedHospital
Indicates that one entity (typically a medical professional, clinic, or organization) is formally connected or associated with a particular hospital for professional or operational purposes.
-
E.
hasHealthcareProvider
Indicates that one entity receives healthcare services or medical oversight from another entity acting as its healthcare provider.
- 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_69f224881b948190b8c4921b250a44a3 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f9fd6834cc8190aa27153d6a99f3bb |
completed | May 5, 2026, 2:23 p.m. |
| PD | Predicate disambiguation | batch_69f7cf769338819092a5f42653dcc956 |
completed | May 3, 2026, 10:43 p.m. |
Created at: April 29, 2026, 7:49 p.m.