Triple
T7181737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virginia Cunningham |
E167462
|
entity |
| Predicate | undergoesTreatment |
P4714
|
FINISHED |
| Object | hydrotherapy |
—
|
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: hydrotherapy | Statement: [Virginia Cunningham, undergoesTreatment, hydrotherapy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: undergoesTreatment Context triple: [Virginia Cunningham, undergoesTreatment, hydrotherapy]
-
A.
treatment
chosen
Indicates that one entity is used as a medical or therapeutic intervention to address, manage, or cure a condition affecting another entity.
-
B.
hasSubsequentTreatment
Indicates that one treatment occurs after and in continuation of another treatment in a temporal sequence.
-
C.
hasCommonTreatment
Indicates that two or more entities share at least one treatment method or therapeutic approach in common.
-
D.
subsequentTreatment
Indicates that one treatment occurs after and in response to a prior treatment or medical event.
-
E.
oftenUndergo
Indicates that an entity frequently experiences, is subjected to, or passes through a particular process, action, or change.
- 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_69c6888a7c548190a3d39b52a393080f |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e9b045c48190b27b2d6f7c11026f |
completed | March 27, 2026, 8:33 p.m. |
| PD | Predicate disambiguation | batch_69c6e74fb0f48190b2ad4dd4efdd241a |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:49 p.m.