Triple
T35412727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | De Materia Medica |
E1023554
|
entity |
| Predicate | describesApproximateNumberOfDrugs |
P108228
|
FINISHED |
| Object | about 600 |
—
|
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: about 600 | Statement: [De Materia Medica, describesApproximateNumberOfDrugs, about 600]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: describesApproximateNumberOfDrugs Context triple: [De Materia Medica, describesApproximateNumberOfDrugs, about 600]
-
A.
numberOfDescribedDrugs
chosen
Indicates the quantity of drugs that are being described or specified in a given context.
-
B.
typicalDoseCount
Indicates the usual number of doses administered or taken in a standard course of use.
-
C.
evaluatedDrug
Indicates that a particular drug has been assessed or tested, typically in the context of a study, experiment, or evaluation process.
-
D.
hasCommonStartingDose_mgPerDay
Indicates that two treatments share the same typical initial dosage, measured in milligrams per day.
-
E.
developedDrugFor
Indicates that one entity created or formulated a drug intended to treat or address a medical condition 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_69f76df54bac8190bd0d3b0eb35cda5f |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fd4129a8848190a5002150278ac689 |
completed | May 8, 2026, 1:49 a.m. |
| PD | Predicate disambiguation | batch_69fd3e0515ec8190937c7af71ebc3875 |
completed | May 8, 2026, 1:36 a.m. |
Created at: May 3, 2026, 4:03 p.m.