Triple
T5849255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iker Casillas |
E129986
|
entity |
| Predicate | sufferedMedicalEvent |
P22468
|
FINISHED |
| Object | myocardial infarction in 2019 |
—
|
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: myocardial infarction in 2019 | Statement: [Iker Casillas, sufferedMedicalEvent, myocardial infarction in 2019]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sufferedMedicalEvent Context triple: [Iker Casillas, sufferedMedicalEvent, myocardial infarction in 2019]
-
A.
hadEvent
chosen
Indicates that an entity experienced, hosted, or was associated with a specific event at some point in time.
-
B.
hasInjuredPerson
Indicates that an entity has a person who has been harmed or injured associated with it.
-
C.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
D.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
E.
hasImmediateMedicalResponse
Indicates that an entity receives prompt medical attention or intervention immediately following an incident or onset of a medical condition.
- 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03c9239e08190bff7ef2bd6d21ae0 |
completed | March 22, 2026, 7:01 p.m. |
| PD | Predicate disambiguation | batch_69c0334412388190bc594794ec5754f9 |
completed | March 22, 2026, 6:21 p.m. |
Created at: March 22, 2026, 3:55 p.m.