Triple
T5931469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aspergillus fumigatus |
E131947
|
entity |
| Predicate | riskFactorForInfection |
P66795
|
FINISHED |
| Object | neutropenia |
—
|
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: neutropenia | Statement: [Aspergillus fumigatus, riskFactorForInfection, neutropenia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: riskFactorForInfection Context triple: [Aspergillus fumigatus, riskFactorForInfection, neutropenia]
-
A.
healthcareWorkerInfectionsApproximate
Indicates that the number of infections among healthcare workers is an approximate or estimated value rather than an exact count.
-
B.
symptomAssociatedWithInfection
Indicates that a particular symptom is linked to, or commonly occurs as a result of, a specific infection.
-
C.
isReservoirOf
Indicates that one entity serves as a storage source or container holding a particular substance, resource, or quantity for another entity or purpose.
-
D.
infectsTissue
Indicates that one entity (typically a pathogen or agent) invades and establishes itself within the tissue of another entity.
-
E.
hasCountryOfRisk
Indicates that an entity is associated with a country where it faces significant exposure, vulnerability, or potential risk.
- 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_69c0085b75e88190a632f9691f9da48b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03c9239e08190bff7ef2bd6d21ae0 |
completed | March 22, 2026, 7:01 p.m. |
| PD | Predicate disambiguation | batch_69c033541d108190a34d1fde2fe9dacb |
completed | March 22, 2026, 6:22 p.m. |
| PDg | Predicate description generation | batch_69c03c8d579081909d7b97fc9014b5d7 |
completed | March 22, 2026, 7:01 p.m. |
Created at: March 22, 2026, 4 p.m.