Triple
T11466662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lowenstein–Jensen medium |
E271796
|
entity |
| Predicate | glycerolEffect |
P99738
|
FINISHED |
| Object | enhances growth of Mycobacterium tuberculosis |
—
|
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: enhances growth of Mycobacterium tuberculosis | Statement: [Lowenstein–Jensen medium, glycerolEffect, enhances growth of Mycobacterium tuberculosis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: glycerolEffect Context triple: [Lowenstein–Jensen medium, glycerolEffect, enhances growth of Mycobacterium tuberculosis]
-
A.
foodEffect
Indicates how consuming a particular food influences or changes another entity, such as an organism, condition, or process.
-
B.
sweetening
Indicates the action or process of making something taste sweeter, often by adding a sweet substance.
-
C.
hasPharmacologicalEffect
Indicates that one entity produces a specific pharmacological effect or action on another entity.
-
D.
fuelEffect
Indicates the influence or impact that a given fuel has on a process, system, or outcome.
-
E.
providesSensoryEffects
Indicates that one entity causes or contributes to sensory experiences or perceptions in another entity.
- F. None of above. chosen
Provenance (4 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f5eb988190b309b8e309f6d1a5 |
completed | April 9, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69d80867ff248190bb157fa9e355353b |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:35 p.m.