Triple

T1169503
Position Surface form Disambiguated ID Type / Status
Subject spinal muscular atrophy E24880 entity
Predicate hasPrognosis P24568 FINISHED
Object variable depending on type and age of onset 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: variable depending on type and age of onset | Statement: [spinal muscular atrophy, hasPrognosis, variable depending on type and age of onset]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasPrognosis
Context triple: [spinal muscular atrophy, hasPrognosis, variable depending on type and age of onset]
  • A. predictedIn
    Indicates that something has been forecast, anticipated, or estimated to occur within or as part of a specified context, time, or situation.
  • B. diagnosedWith
    Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
  • C. hasProposedCause
    Indicates that one entity is suggested or hypothesized to be the cause or explanation for another entity or event.
  • D. hasConsequence
    Indicates that one event, action, or condition leads to or results in another as its outcome or effect.
  • 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. 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_69a494082a7c819095004f423f294a64 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bce821b481908bc278a3fa7973f4 completed March 1, 2026, 10:25 p.m.
PD Predicate disambiguation batch_69a4bb5656948190b0b1d5446ad06005 completed March 1, 2026, 10:19 p.m.
PDg Predicate description generation batch_69a4bbd7ff1881908c943ecdfea59e81 completed March 1, 2026, 10:21 p.m.
Created at: March 1, 2026, 7:45 p.m.