Triple
T34074761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acid Queen |
E873877
|
entity |
| Predicate | methodOfCure |
P84200
|
FINISHED |
| Object | hallucinogenic excess |
—
|
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: hallucinogenic excess | Statement: [Acid Queen, methodOfCure, hallucinogenic excess]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: methodOfCure Context triple: [Acid Queen, methodOfCure, hallucinogenic excess]
-
A.
typeOfRemedy
Indicates that one entity is a specific kind or category of remedy in relation to another entity.
-
B.
knownForTreatmentOf
Indicates that an entity is recognized or notable for providing treatment or medical care for a particular condition, disease, or type of patient.
-
C.
initialRemedy
Indicates that a particular remedy is the first or primary treatment applied or prescribed in response to a condition or problem.
-
D.
hasRemedy
Indicates that one entity serves as a remedy, treatment, or corrective measure for a problem, condition, or undesirable state associated with another entity.
-
E.
curedWith
chosen
Indicates that one entity is treated or healed by using another entity as the remedy or therapeutic method.
- 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_69f349a566808190a1c63b898f33cddf |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71fb1ab3881908e2f7c0e6f23db49 |
completed | May 3, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f71cc6397881909aaad37a9daa8a7e |
completed | May 3, 2026, 10 a.m. |
Created at: May 1, 2026, 1:52 a.m.