Triple
T23154051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Craig Cunningham |
E578393
|
entity |
| Predicate | medicalEventContext |
P126644
|
FINISHED |
| Object | pre-game warmup |
—
|
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: pre-game warmup | Statement: [Craig Cunningham, medicalEventContext, pre-game warmup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalEventContext Context triple: [Craig Cunningham, medicalEventContext, pre-game warmup]
-
A.
medicalEvent
Indicates that a specific health-related occurrence or clinical incident has taken place involving one or more entities.
-
B.
clinicalContext
chosen
Indicates the medical situation, setting, or circumstances under which a clinical finding, observation, or action occurs.
-
C.
diseaseContext
Indicates that the relationship or action occurs within, is influenced by, or is specifically relevant to a particular disease or pathological condition.
-
D.
medicalText
Indicates that the subject is a piece of text whose content is medical in nature, such as clinical, diagnostic, or health-related information.
-
E.
clinicalSetting
Indicates that an action, observation, or interaction occurs within a healthcare or medical care environment.
- 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_69e245fb8de081908f0eba7b5fd75bc4 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18efbe9a08190bcb6e822b8eab544 |
completed | April 29, 2026, 4:54 a.m. |
| PD | Predicate disambiguation | batch_69ef89ff76808190808ee4ad9dea776b |
completed | April 27, 2026, 4:08 p.m. |
Created at: April 17, 2026, 4:01 p.m.