Triple
T23186811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | REGN-COV2 |
E579612
|
entity |
| Predicate | reducedEffectivenessAgainst |
P84339
|
FINISHED |
| Object | some later SARS-CoV-2 variants |
—
|
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: some later SARS-CoV-2 variants | Statement: [REGN-COV2, reducedEffectivenessAgainst, some later SARS-CoV-2 variants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reducedEffectivenessAgainst Context triple: [REGN-COV2, reducedEffectivenessAgainst, some later SARS-CoV-2 variants]
-
A.
effectivenessAgainst
chosen
Indicates how well one entity performs in countering, influencing, or mitigating the impact of another entity.
-
B.
opposesEffectOf
Indicates that one entity counteracts, reduces, or nullifies the effect produced by another entity.
-
C.
usedAgainst
Indicates that one entity is employed, applied, or deployed in opposition to, or for the purpose of affecting, another entity.
-
D.
reasonForIneffectiveness
Indicates that one entity specifies the cause or explanation for why another entity is ineffective or fails to achieve its intended effect.
-
E.
limitedEffectOn
Indicates that one entity’s influence, impact, or consequence on another is small in magnitude, scope, or significance.
- 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_69e245ff8000819090d12008805315b7 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18fd3aaa08190b9cf7afe4ee5a38d |
completed | April 29, 2026, 4:57 a.m. |
| PD | Predicate disambiguation | batch_69ef8a041c0081909afb670d17a5aaba |
completed | April 27, 2026, 4:08 p.m. |
Created at: April 17, 2026, 4:05 p.m.