Triple
T29019899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amoebozoa |
E737421
|
entity |
| Predicate | includesPathogenExample |
P199617
|
FINISHED |
| Object | Entamoeba histolytica |
—
|
NE NERFINISHED |
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: Entamoeba histolytica | Statement: [Amoebozoa, includesPathogenExample, Entamoeba histolytica]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesPathogenExample Context triple: [Amoebozoa, includesPathogenExample, Entamoeba histolytica]
-
A.
includesPathogensOf
Indicates that one entity contains or encompasses the pathogens that are associated with or originate from another entity.
-
B.
includesPathogens
chosen
Indicates that the subject entity contains, carries, or is associated with one or more pathogenic organisms.
-
C.
isPathogenOf
Indicates that one entity is a disease-causing agent (pathogen) that infects or causes illness in another entity.
-
D.
carriesPathogen
Indicates that one entity harbors and can transmit a disease-causing pathogen to another entity or environment.
-
E.
pathogenType
Indicates the specific kind or category of pathogen associated with or responsible for an entity or condition.
- 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_69f077ee19f881909af48f9cab00a2e5 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69ff63225b6481909217ad11b4f7d3ba |
completed | May 9, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69ff60e0882c819085d097010db43ee0 |
completed | May 9, 2026, 4:29 p.m. |
Created at: April 28, 2026, 9:48 a.m.