Triple
T2504155
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mycobacterium tuberculosis |
E52537
|
entity |
| Predicate | zoonoticPotential |
P40200
|
FINISHED |
| Object | can infect animals |
—
|
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: can infect animals | Statement: [Mycobacterium tuberculosis, zoonoticPotential, can infect animals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: zoonoticPotential Context triple: [Mycobacterium tuberculosis, zoonoticPotential, can infect animals]
-
A.
pathogenicityToHumans
Indicates that an entity has the capacity to cause disease or harmful health effects in humans.
-
B.
isReservoirOf
Indicates that one entity serves as a storage source or container holding a particular substance, resource, or quantity for another entity or purpose.
-
C.
pathogenType
Indicates the specific kind or category of pathogen associated with or responsible for an entity or condition.
-
D.
speciesType
Indicates the specific biological species category to which an entity belongs.
-
E.
isEndemicTo
Indicates that something naturally occurs and is restricted to a particular geographic area or region.
- 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1cd2db0819087d21ec49ffd9585 |
completed | March 7, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69abd0bba5348190bb4637d3165cb339 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd1aa13a08190a8757c017d8a2478 |
completed | March 7, 2026, 7:20 a.m. |
Created at: March 6, 2026, 9:46 p.m.