Triple
T36599684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | mosunetuzumab |
E902883
|
entity |
| Predicate | immuneEffect |
P185920
|
FINISHED |
| Object | forms immunologic synapse between T cells and B cells |
—
|
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: forms immunologic synapse between T cells and B cells | Statement: [mosunetuzumab, immuneEffect, forms immunologic synapse between T cells and B cells]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: immuneEffect Context triple: [mosunetuzumab, immuneEffect, forms immunologic synapse between T cells and B cells]
-
A.
immunity
Indicates that an entity is protected against or not affected by a particular agent, condition, or influence.
-
B.
immuneResponseType
Indicates the specific kind or category of immune reaction that occurs in response to a particular stimulus or agent.
-
C.
immunityType
Indicates the specific kind or category of immunity that applies in a given context (e.g., legal, diplomatic, medical).
-
D.
impactOnNeutralizingAntibodies
Indicates the effect that something has on the presence, level, or effectiveness of neutralizing antibodies.
-
E.
healthEffect
Indicates the impact or consequence that one entity has on the health or well-being of another.
- 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_69f76e66b7b88190848f7a3e1188915f |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1baf25c8190a78dd54a400d2c50 |
completed | May 3, 2026, 9:44 p.m. |
| PDg | Predicate description generation | batch_69f7c3705b5c81908c84004543a71c07 |
completed | May 3, 2026, 9:51 p.m. |
Created at: May 3, 2026, 4:11 p.m.