Triple
T3172996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Noah Shebib |
E66396
|
entity |
| Predicate | hasHealthCondition |
P1005
|
FINISHED |
| Object | multiple sclerosis |
—
|
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: multiple sclerosis | Statement: [Noah Shebib, hasHealthCondition, multiple sclerosis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHealthCondition Context triple: [Noah Shebib, hasHealthCondition, multiple sclerosis]
-
A.
hasHealthConcern
Indicates that an entity has a specific health-related issue, condition, or concern associated with it.
-
B.
diagnosedWith
chosen
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
C.
hasTargetDisease
Indicates that an entity (such as a treatment, study, or intervention) is directed toward, intended to affect, or primarily concerned with a specified disease.
-
D.
hasHistoryOf
Indicates that an entity has a documented prior occurrence or background of a specified condition, event, or state.
-
E.
mayBeComorbidWith
Indicates that two conditions or disorders can occur together in the same individual, potentially influencing each other’s presence or severity.
- 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_69ad8586a34c8190944c63ec11a8de1a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada66facf881908b9ec687d68ce91b |
completed | March 8, 2026, 4:40 p.m. |
| PD | Predicate disambiguation | batch_69ad9e0076b4819094628f1ad10b8f68 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:06 p.m.