Triple
T4065742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MMRTG |
E86318
|
entity |
| Predicate | usesEffect |
P53880
|
FINISHED |
| Object | Seebeck effect |
—
|
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: Seebeck effect | Statement: [MMRTG, usesEffect, Seebeck effect]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesEffect Context triple: [MMRTG, usesEffect, Seebeck effect]
-
A.
usesEffectType
Indicates that an entity employs or is associated with a particular type or category of effect in its operation or behavior.
-
B.
sideEffect
Indicates that one entity is an unintended or secondary effect resulting from the use or occurrence of another entity.
-
C.
tookEffect
Indicates that a change, rule, condition, or event became active, operative, or started producing its intended consequences.
-
D.
hasCommonSideEffect
Indicates that two or more treatments, drugs, or interventions share at least one side effect in common.
-
E.
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.
- 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_69aed93c69208190a4efac0efe3cd69b |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefd0bdea48190805a79515ee92709 |
completed | March 9, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69aef90438908190a005b08ba271eacf |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefd0995188190b1bc8771fe7f423a |
completed | March 9, 2026, 5:02 p.m. |
Created at: March 9, 2026, 3:38 p.m.