Triple
T4482808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yayoi Kusama |
E100171
|
entity |
| Predicate | hasMentalHealthCondition |
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: [Yayoi Kusama, hasMentalHealthCondition, obsessive-compulsive disorder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMentalHealthCondition Context triple: [Yayoi Kusama, hasMentalHealthCondition, 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.
hasPossibleSymptom
Indicates that an entity (such as a condition or disease) may be associated with a particular symptom that can potentially occur.
-
D.
hasMood
Indicates that an entity is experiencing or characterized by a particular emotional or affective state.
-
E.
hasHistoryOf
Indicates that an entity has a documented prior occurrence or background of a specified condition, event, or state.
- 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_69b34553cbe48190afa8ac1cac285b86 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35728ed508190ba0e882fa62d8848 |
completed | March 13, 2026, 12:15 a.m. |
| PD | Predicate disambiguation | batch_69b3563d63008190816e37027e761375 |
completed | March 13, 2026, 12:11 a.m. |
Created at: March 12, 2026, 11:36 p.m.