Triple
T2504149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mycobacterium tuberculosis |
E52537
|
entity |
| Predicate | virulenceFactor |
P40198
|
FINISHED |
| Object | ESX-1 secretion system |
—
|
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: ESX-1 secretion system | Statement: [Mycobacterium tuberculosis, virulenceFactor, ESX-1 secretion system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: virulenceFactor Context triple: [Mycobacterium tuberculosis, virulenceFactor, ESX-1 secretion system]
-
A.
virulence
Indicates the degree to which a pathogen or harmful agent is capable of causing damage, disease, or severe effects in its host.
-
B.
pathogenType
Indicates the specific kind or category of pathogen associated with or responsible for an entity or condition.
-
C.
pathogenicityToHumans
Indicates that an entity has the capacity to cause disease or harmful health effects in humans.
-
D.
pathogenGenus
Indicates that one entity is a pathogen belonging to, or classified under, the specified genus.
-
E.
infectsTissue
Indicates that one entity (typically a pathogen or agent) invades and establishes itself within the tissue of 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_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.