Triple
T6469743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tsimané people |
E142316
|
entity |
| Predicate | healthFinding |
P33305
|
FINISHED |
| Object | among the lowest recorded rates of atherosclerosis |
—
|
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: among the lowest recorded rates of atherosclerosis | Statement: [Tsimané people, healthFinding, among the lowest recorded rates of atherosclerosis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: healthFinding Context triple: [Tsimané people, healthFinding, among the lowest recorded rates of atherosclerosis]
-
A.
healthIndicator
chosen
Indicates a measure or signal that reflects the health status or condition of an entity.
-
B.
clinicalSignOf
Indicates that one clinical sign is evidence or manifestation of a particular disease, condition, or underlying medical state.
-
C.
hasHealthConcern
Indicates that an entity has a specific health-related issue, condition, or concern associated with it.
-
D.
healthEffect
Indicates the impact or consequence that one entity has on the health or well-being of another.
-
E.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
- 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_69c008d3bf4c8190bcf798c5ba9d6fb3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06a16272c81909313455002cd884d |
completed | March 22, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69c0673d46a08190bc8bcd29f9555fe7 |
completed | March 22, 2026, 10:03 p.m. |
Created at: March 22, 2026, 4:50 p.m.