Triple
T30087596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | lenticulostriate arteries |
E764638
|
entity |
| Predicate | riskFactorForInjury |
P149293
|
FINISHED |
| Object | long-standing hypertension |
—
|
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: long-standing hypertension | Statement: [lenticulostriate arteries, riskFactorForInjury, long-standing hypertension]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: riskFactorForInjury Context triple: [lenticulostriate arteries, riskFactorForInjury, long-standing hypertension]
-
A.
causeOfInjury
Indicates that one entity is the source or reason that another entity sustained an injury.
-
B.
injuryType
Indicates the specific kind or category of injury associated with an entity or event.
-
C.
hasRiskFactorFor
chosen
Indicates that one entity contributes to or increases the likelihood of another entity experiencing a particular risk or adverse outcome.
-
D.
riskFactorForInfection
Indicates that something increases the likelihood or susceptibility of an entity to develop a particular infection.
-
E.
riskElement
Indicates that one entity is a risk-related component, factor, or contributor associated with another entity within a risk context.
- 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_69f22473c0fc8190a926a8051b3b378b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67d6de8a081909e426dcdf9fe0536 |
completed | May 2, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f673c664f08190b4d66cdc305e10db |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 29, 2026, 7:04 p.m.