Triple
T23278500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eurycoma |
E588788
|
entity |
| Predicate | isUsedInTraditionalMedicine |
P130888
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Eurycoma, isUsedInTraditionalMedicine, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isUsedInTraditionalMedicine Context triple: [Eurycoma, isUsedInTraditionalMedicine, true]
-
A.
languageOfTraditionalUse
Indicates the language traditionally used or associated with a given entity, such as a work, practice, or community.
-
B.
partUsedMedicinally
Indicates that a specific part of an entity (such as a plant or organism) is used for medicinal purposes.
-
C.
medicinalUse
Indicates that one entity is used as a treatment or remedy for a disease, condition, or health-related purpose affecting another entity.
-
D.
isMedicinalPlant
chosen
Indicates that a plant is used for medicinal purposes, such as treating, preventing, or alleviating health conditions.
-
E.
practicedMedicineOfTradition
Indicates that an entity engaged in the medical practices or healing methods associated with a particular cultural or traditional 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_69e25d16e2c08190a291de254703129e |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1957991108190ac82fa6dd355f722 |
completed | April 29, 2026, 5:22 a.m. |
| PD | Predicate disambiguation | batch_69effcecabd88190856fb6e1d993e4dd |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:49 p.m.