Triple
T12763924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legionella |
E305069
|
entity |
| Predicate | infectionTargetCell |
P6647
|
FINISHED |
| Object | human alveolar macrophages |
—
|
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: human alveolar macrophages | Statement: [Legionella, infectionTargetCell, human alveolar macrophages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: infectionTargetCell Context triple: [Legionella, infectionTargetCell, human alveolar macrophages]
-
A.
infectsTissue
chosen
Indicates that one entity (typically a pathogen or agent) invades and establishes itself within the tissue of another entity.
-
B.
infectionMethod
Indicates the way or mechanism by which an infection is transmitted or established from a source to a host.
-
C.
infectionType
Indicates the specific category or nature of an infection associated with an entity or event.
-
D.
primaryInfectionStructure
Indicates the structure that serves as the main or initial means by which an infection is established in a host.
-
E.
isPathogenOf
Indicates that one entity is a disease-causing agent (pathogen) that infects or causes illness in 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_69d7bdf1fcd081909ffb0e0d6fa3a07d |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d8f9f588190bffdea878856204b |
completed | April 10, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69d96409739881909174ba005a986cb5 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:28 p.m.