Triple
T2814934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Multi-Ethnic Study of Atherosclerosis |
E54258
|
entity |
| Predicate | aimsToInform |
P43779
|
FINISHED |
| Object | cardiovascular disease prevention guidelines |
—
|
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 disease prevention guidelines | Statement: [Multi-Ethnic Study of Atherosclerosis, aimsToInform, cardiovascular disease prevention guidelines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aimsToInform Context triple: [Multi-Ethnic Study of Atherosclerosis, aimsToInform, cardiovascular disease prevention guidelines]
-
A.
aimsToExplain
Indicates that one entity intends to clarify, make understandable, or provide an explanation about another entity, concept, or situation.
-
B.
aimsToProvide
Indicates that one entity intends or is designed to supply, deliver, or make available something to another entity.
-
C.
aimsToCritique
Indicates an intention to analyze and point out faults, limitations, or weaknesses in something.
-
D.
aimsToProtect
Indicates an intention or purpose to safeguard or defend one entity, value, or condition from harm, risk, or undesirable outcomes.
-
E.
aimsToSolve
Indicates an intention or purpose directed toward resolving, addressing, or eliminating a particular problem, challenge, or issue.
- 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_69ab49de0af08190b3da69683be1e728 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde4d29488190a32461906dd9ea7e |
completed | March 7, 2026, 8:14 a.m. |
| PD | Predicate disambiguation | batch_69abdd0740208190911dc9c9546a79ae |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abde0f4c648190b9812e64f30c39da |
completed | March 7, 2026, 8:13 a.m. |
Created at: March 6, 2026, 9:59 p.m.