Triple
T27089921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Violet Turner |
E686135
|
entity |
| Predicate | hasMentalHealthTheme |
P62989
|
FINISHED |
| Object | Trauma |
—
|
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: Trauma | Statement: [Dr. Violet Turner, hasMentalHealthTheme, Trauma]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMentalHealthTheme Context triple: [Dr. Violet Turner, hasMentalHealthTheme, Trauma]
-
A.
hasPsychologicalCondition
Indicates that an entity experiences or is diagnosed with a particular psychological or mental health condition.
-
B.
hasPsychiatricComponent
Indicates that something includes, involves, or is associated with a psychiatric aspect, factor, or condition as part of its overall nature or composition.
-
C.
hasThematicConcern
chosen
Indicates that one entity (such as a work, text, or discourse) centrally involves, addresses, or focuses on a particular theme, issue, or subject as a primary concern.
-
D.
hasPsychologicalImpact
Indicates that one entity causes or contributes to a mental or emotional effect on another entity.
-
E.
hasMood
Indicates that an entity is experiencing or characterized by a particular emotional or affective 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_69ef148940ec819097b5c20fbfbf7c81 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f73ae120bc8190bff94d38d7a7a00d |
completed | May 3, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69f73a38d0848190aa5139144b8561c6 |
completed | May 3, 2026, 12:06 p.m. |
Created at: April 27, 2026, 8:40 a.m.