Triple
T4306656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ancylostoma duodenale |
E99969
|
entity |
| Predicate | pathogenesisMechanism |
P22707
|
FINISHED |
| Object | chronic intestinal blood loss |
—
|
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: chronic intestinal blood loss | Statement: [Ancylostoma duodenale, pathogenesisMechanism, chronic intestinal blood loss]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pathogenesisMechanism Context triple: [Ancylostoma duodenale, pathogenesisMechanism, chronic intestinal blood loss]
-
A.
pathogenicity
Indicates that one entity has the capacity to cause disease or harmful pathological effects in another entity.
-
B.
pathologyFeature
chosen
Indicates that one entity is a pathological characteristic, sign, or abnormal finding associated with another entity in a medical or biological context.
-
C.
pathogenType
Indicates the specific kind or category of pathogen associated with or responsible for an entity or condition.
-
D.
pathogenicityToHumans
Indicates that an entity has the capacity to cause disease or harmful health effects in humans.
-
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_69b345528ebc8190b5abc7e95094792d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350bb78cc8190a850aca47d8711cf |
completed | March 12, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69b347ff45cc8190b0cc335a94cc3d73 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:09 p.m.