Triple
T33619512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lesley Brown |
E861222
|
entity |
| Predicate | usedMedicalProcedure |
P53036
|
FINISHED |
| Object | in vitro fertilization |
—
|
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: in vitro fertilization | Statement: [Lesley Brown, usedMedicalProcedure, in vitro fertilization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedMedicalProcedure Context triple: [Lesley Brown, usedMedicalProcedure, in vitro fertilization]
-
A.
hadProcedure
chosen
Indicates that a subject underwent or received a specific medical or clinical procedure.
-
B.
usedMedicalPersonnel
Indicates that an entity employed or made use of medical personnel in performing an action or providing a service.
-
C.
diseaseUsed
Indicates that a particular disease is employed or utilized as a tool, model, or condition within a given context or process.
-
D.
usesTreatment
Indicates that one entity applies or employs a particular treatment or therapeutic method on or for another entity.
-
E.
usesMedicalKnowledge
Indicates that an entity applies or relies on medical knowledge in performing an action or making a decision.
- 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_69f34980fabc81909819228729a9ca84 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f81ace388190ad2dac7b9da78e19 |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:41 a.m.