Triple
T22691765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lark |
E561067
|
entity |
| Predicate | hasRiskFactorFor |
P149293
|
FINISHED |
| Object | cardiovascular disease |
—
|
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 | Statement: [Lark, hasRiskFactorFor, cardiovascular disease]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRiskFactorFor Context triple: [Lark, hasRiskFactorFor, cardiovascular disease]
-
A.
hasRiskFrom
Indicates that one entity is exposed to or may suffer potential harm, loss, or adverse effects as a result of another entity.
-
B.
riskFactorForDeath
Indicates that something increases the likelihood or probability of death occurring.
-
C.
riskFactorForInfection
Indicates that something increases the likelihood or susceptibility of an entity to develop a particular infection.
-
D.
hasAssociatedDisease
Indicates that an entity is linked to, or commonly occurs with, a particular disease or medical condition.
-
E.
riskFactorTypeStudied
Indicates that a particular type of risk factor is the subject of study or analysis in a given context.
- 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_69e2454d71b48190a1f80af9f82b6fcf |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1789adcc48190b4a717166d5dba19 |
completed | April 29, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69ee62b2259c819091ed1387a748b9f3 |
completed | April 26, 2026, 7:08 p.m. |
| PDg | Predicate description generation | batch_69ee8843d3308190b6e22bb98ae5c3d8 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 3:13 p.m.