Triple
T25425254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RDSEED |
E637100
|
entity |
| Predicate | sideEffects |
P39645
|
FINISHED |
| Object | may update ZF, SF, OF, AF, PF as undefined |
—
|
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 update ZF, SF, OF, AF, PF as undefined | Statement: [RDSEED, sideEffects, may update ZF, SF, OF, AF, PF as undefined]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sideEffects Context triple: [RDSEED, sideEffects, may update ZF, SF, OF, AF, PF as undefined]
-
A.
sideEffect
Indicates that one entity is an unintended or secondary effect resulting from the use or occurrence of another entity.
-
B.
sideEffectManagement
Indicates the relationship in which an action or intervention is used to monitor, reduce, or control the side effects caused by another action, treatment, or condition.
-
C.
possibleSideEffect
chosen
Indicates that one entity may occur as a side effect or unintended consequence of another entity or action.
-
D.
hasCommonSideEffect
Indicates that two or more treatments, drugs, or interventions share at least one side effect in common.
-
E.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
- 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_69e75db58a1c8190891b9ff7c2f8414e |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f6bf25c881909f049d5393927bfb |
completed | May 2, 2026, 1:06 p.m. |
| PD | Predicate disambiguation | batch_69f45d0dbc8c8190beecce679fce90a4 |
completed | May 1, 2026, 7:58 a.m. |
Created at: April 21, 2026, 1:57 p.m.