Triple
T17361662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alonzo Mourning |
E422079
|
entity |
| Predicate | hasSeriousMedicalCondition |
P4720
|
FINISHED |
| Object | kidney disease |
—
|
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: kidney disease | Statement: [Alonzo Mourning, hasSeriousMedicalCondition, kidney disease]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeriousMedicalCondition Context triple: [Alonzo Mourning, hasSeriousMedicalCondition, kidney disease]
-
A.
hadCondition
Indicates that an entity experienced or was diagnosed with a particular medical or health-related condition.
-
B.
hasHealthConcern
chosen
Indicates that an entity has a specific health-related issue, condition, or concern associated with it.
-
C.
hasSeriousSideEffect
Indicates that an entity (such as a treatment, drug, or intervention) causes or is associated with a significant or severe adverse effect on another entity (typically a patient or biological system).
-
D.
hasAssociatedDisease
Indicates that an entity is linked to, or commonly occurs with, a particular disease or medical condition.
-
E.
hasTypicalConditions
Indicates that something is associated with conditions or circumstances that are commonly or normally present for it.
- 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_69d889d520008190a26917a95bf1c2ea |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a4d6de88190b816b93ff5778157 |
completed | April 19, 2026, 2:13 a.m. |
| PD | Predicate disambiguation | batch_69e3b02662d08190a07d0fb5c04b6f33 |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:44 a.m.