Triple
T2403787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stelara |
E50227
|
entity |
| Predicate | hasSeriousRisk |
P24552
|
FINISHED |
| Object | serious infections |
—
|
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: serious infections | Statement: [Stelara, hasSeriousRisk, serious infections]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeriousRisk Context triple: [Stelara, hasSeriousRisk, serious infections]
-
A.
hasSeriousSideEffect
chosen
Indicates that an entity (such as a treatment, drug, or intervention) causes or is associated with a significant or severe adverse effect on another entity (typically a patient or biological system).
-
B.
hasSevereWeatherRisk
Indicates that an entity is exposed to or associated with a high likelihood of severe or hazardous weather conditions.
-
C.
hasCountryOfRisk
Indicates that an entity is associated with a country where it faces significant exposure, vulnerability, or potential risk.
-
D.
riskLevel
Indicates the degree of potential harm, loss, or adverse outcome associated with a particular situation, action, or entity.
-
E.
riskType
Indicates the category or nature of risk associated with an entity, event, or relationship.
- 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_69a88b0339a88190a1207333cd271cc9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abceab9ce881909ae0a2f34515c11e |
completed | March 7, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69abc5a530e8819094105aa92dfaf6b3 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:58 p.m.