Triple

T3342780
Position Surface form Disambiguated ID Type / Status
Subject Adrian Monk E70296 entity
Predicate hasDisorder P4720 FINISHED
Object obsessive-compulsive disorder 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: obsessive-compulsive disorder | Statement: [Adrian Monk, hasDisorder, obsessive-compulsive disorder]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasDisorder
Context triple: [Adrian Monk, hasDisorder, obsessive-compulsive disorder]
  • A. diagnosedWith
    Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
  • B. hasHealthConcern chosen
    Indicates that an entity has a specific health-related issue, condition, or concern associated with it.
  • 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. mayBeComorbidWith
    Indicates that two conditions or disorders can occur together in the same individual, potentially influencing each other’s presence or severity.
  • E. hasContraindication
    Indicates that one entity (such as a treatment, drug, or procedure) should not be used or performed in the presence of another entity (such as a condition, factor, or co-medication) because it may cause harm or adverse effects.
  • 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_69ad85a405e48190b6e68de7cf9f319e completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb1f06f8c8190a6b7c56ac3f5ff07 completed March 8, 2026, 5:29 p.m.
PD Predicate disambiguation batch_69ada42df1d48190874bb05f95deefde completed March 8, 2026, 4:30 p.m.
Created at: March 8, 2026, 3:12 p.m.