Triple
T10655132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | shooting of Pope John Paul II |
E251067
|
entity |
| Predicate | medicalConsequence |
P812
|
FINISHED |
| Object | Pope John Paul II underwent emergency surgery |
—
|
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: Pope John Paul II underwent emergency surgery | Statement: [shooting of Pope John Paul II, medicalConsequence, Pope John Paul II underwent emergency surgery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalConsequence Context triple: [shooting of Pope John Paul II, medicalConsequence, Pope John Paul II underwent emergency surgery]
-
A.
hasConsequence
chosen
Indicates that one event, action, or condition leads to or results in another as its outcome or effect.
-
B.
healthEffect
Indicates the impact or consequence that one entity has on the health or well-being of another.
-
C.
medicalEvent
Indicates that a specific health-related occurrence or clinical incident has taken place involving one or more entities.
-
D.
clinicalSignOf
Indicates that one clinical sign is evidence or manifestation of a particular disease, condition, or underlying medical state.
-
E.
medicalText
Indicates that the subject is a piece of text whose content is medical in nature, such as clinical, diagnostic, or health-related information.
- 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_69d6aa5a4c4881908f39be6efe5981e5 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dff94c188190b1a822c3720d18b7 |
completed | April 8, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69d6dd8753108190b799ffa0c760526e |
completed | April 8, 2026, 10:58 p.m. |
Created at: April 8, 2026, 9:06 p.m.