Triple
T22717741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aubagio |
E561778
|
entity |
| Predicate | hasTeratogenicRisk |
P149429
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Aubagio, hasTeratogenicRisk, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTeratogenicRisk Context triple: [Aubagio, hasTeratogenicRisk, true]
-
A.
hasPregnancyRisk
Indicates that one entity poses or is associated with a potential risk of causing pregnancy for another entity.
-
B.
hasRiskFrom
Indicates that one entity is exposed to or may suffer potential harm, loss, or adverse effects as a result of another entity.
-
C.
hasCommonAdverseEffect
Indicates that two or more entities share at least one adverse effect that occurs in response to them.
-
D.
notableToxicity
Indicates that an entity is recognized for having a significant level or history of toxicity, harm, or detrimental effects in its context or interactions.
-
E.
isHazardTo
Indicates that one entity poses a potential source of danger, harm, or risk to another entity.
- 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_69e2454fc984819088213b58ee87a002 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1790ecbc48190926d16b20b674dbd |
completed | April 29, 2026, 3:20 a.m. |
| PD | Predicate disambiguation | batch_69ee62bd657c81909f7b01245b080a5f |
completed | April 26, 2026, 7:08 p.m. |
| PDg | Predicate description generation | batch_69ee8843d3308190b6e22bb98ae5c3d8 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 3:19 p.m.