Triple
T36599917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peyer’s patches |
E902888
|
entity |
| Predicate | roleInVaccineResponse |
—
|
GENERATED |
| Object | target for oral vaccines |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInVaccineResponse Context triple: [Peyer’s patches, roleInVaccineResponse, target for oral vaccines]
-
A.
roleInAppointments
Indicates the specific function or capacity an entity holds within one or more scheduled appointments.
-
B.
roleInRepertoire
Indicates that an entity serves a specific role or function within a larger repertoire, collection, or set of items.
-
C.
roleInvolves
Indicates that a particular role includes or requires participation in a specified activity, responsibility, or function.
-
D.
roleInName
Indicates that a specific role, title, or position is included as part of an entity’s name or naming expression.
-
E.
roleAtGV
Indicates that an entity holds or held a specific role or position at a particular organization, institution, or venue referred to as GV.
- F. None of above. chosen
Provenance (1 batch)
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_69f76e66b7b88190848f7a3e1188915f |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:11 p.m.