Triple
T15927777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lex Luger |
E386245
|
entity |
| Predicate | sufferedMajorHealthIssue |
P91920
|
FINISHED |
| Object | spinal infarction in 2007 |
—
|
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: spinal infarction in 2007 | Statement: [Lex Luger, sufferedMajorHealthIssue, spinal infarction in 2007]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sufferedMajorHealthIssue Context triple: [Lex Luger, sufferedMajorHealthIssue, spinal infarction in 2007]
-
A.
hasHealthConcern
Indicates that an entity has a specific health-related issue, condition, or concern associated with it.
-
B.
sufferedCondition
chosen
Indicates that an entity experienced or was afflicted by a particular condition, typically adverse or harmful, at some point in time.
-
C.
hadCondition
Indicates that an entity experienced or was diagnosed with a particular medical or health-related condition.
-
D.
focusesOnMedicalCare
Indicates that one entity directs attention, resources, or activity specifically toward providing or improving medical care for another entity.
-
E.
hadIssue
Indicates that an entity experienced, encountered, or was affected by a particular problem, defect, or difficulty.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e172b48b308190bc430b2308cbc75b |
completed | April 16, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69e142cf5c548190a931f7b58144cd31 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:52 a.m.