Triple
T32451192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microdosing |
E829288
|
entity |
| Predicate | hasTypicalSubstance |
P113728
|
FINISHED |
| Object | Lysergic acid diethylamide |
—
|
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: Lysergic acid diethylamide | Statement: [Microdosing, hasTypicalSubstance, Lysergic acid diethylamide]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalSubstance Context triple: [Microdosing, hasTypicalSubstance, Lysergic acid diethylamide]
-
A.
hasSubstance
chosen
Indicates that one entity contains, consists of, or is composed of a particular substance or material.
-
B.
requiresSubstance
Indicates that one entity depends on or needs a particular substance in order to function, occur, or be valid.
-
C.
substanceType
Indicates the specific kind or category of substance associated with an entity or relation.
-
D.
hasFictionalSubstance
Indicates that one entity includes, contains, or involves a fictional or imaginary substance as part of its composition, setting, or narrative.
-
E.
primarySubstanceExample
Indicates that one entity serves as the main illustrative example of a particular substance associated with another entity.
- 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_69fec4cffed08190b5e5e7cc0c87493e |
completed | May 9, 2026, 5:23 a.m. |
| PD | Predicate disambiguation | batch_69fec2ea7fe08190bd751b39515f69d1 |
completed | May 9, 2026, 5:15 a.m. |
Created at: May 1, 2026, 12:56 a.m.