Triple
T32148340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Egermann |
E821083
|
entity |
| Predicate | hasTherapeuticSessionsWith |
P154436
|
FINISHED |
| Object | psychiatrist |
—
|
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: psychiatrist | Statement: [Peter Egermann, hasTherapeuticSessionsWith, psychiatrist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTherapeuticSessionsWith Context triple: [Peter Egermann, hasTherapeuticSessionsWith, psychiatrist]
-
A.
hasTherapyContext
Indicates that an entity is associated with, occurs within, or is characterized by a specific therapeutic setting, framework, or treatment context.
-
B.
hasPatient
Indicates that an action, event, or process involves a specific entity as the one undergoing or receiving its effects (the patient).
-
C.
hasTherapeuticGoal
Indicates that an action, treatment, or intervention is undertaken with the intention of achieving a specific therapeutic or health-related outcome.
-
D.
includesTherapy
Indicates that one entity contains, offers, or encompasses a specific therapeutic treatment or intervention as part of its scope or composition.
-
E.
hasMeetings
chosen
Indicates that an entity participates in or is associated with one or more scheduled meetings.
- 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_69f3490520d081909b2f1271dab75faa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7117e55908190a67105e92bc4830f |
completed | May 3, 2026, 9:12 a.m. |
| PD | Predicate disambiguation | batch_69f70f380690819090cc34763ba460ed |
completed | May 3, 2026, 9:02 a.m. |
Created at: May 1, 2026, 12:31 a.m.