Triple
T32451212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microdosing |
E829288
|
entity |
| Predicate | hasReportedEffect |
P139133
|
FINISHED |
| Object | Subtle changes in perception |
—
|
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: Subtle changes in perception | Statement: [Microdosing, hasReportedEffect, Subtle changes in perception]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReportedEffect Context triple: [Microdosing, hasReportedEffect, Subtle changes in perception]
-
A.
hasEffectIn
Indicates that one entity produces, causes, or exerts an effect within a specified context, system, or environment.
-
B.
hasIntendedEffect
Indicates that one entity is expected or designed to produce a particular effect or outcome on another entity or context.
-
C.
hadEffectUntil
Indicates that an effect or condition held true up to a specific time or event, after which it no longer applied.
-
D.
hasAdverseEffects
Indicates that one entity causes or is associated with harmful, negative, or undesired effects on another entity.
-
E.
hasEffectText
chosen
Indicates that a subject is associated with a textual description specifying its effect or impact.
- 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_69f3491d2e5c819092b1c9535beff8ec |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a0245e917748190bf0a65db538aa66f |
completed | May 11, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_6a023f7cf7148190af5c2ea501511145 |
completed | May 11, 2026, 8:43 p.m. |
Created at: May 1, 2026, 12:56 a.m.