Triple
T18785534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheffield Children’s Hospital |
E459363
|
entity |
| Predicate | patientAgeRange |
P2736
|
FINISHED |
| Object | 0–18 years |
—
|
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: 0–18 years | Statement: [Sheffield Children’s Hospital, patientAgeRange, 0–18 years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: patientAgeRange Context triple: [Sheffield Children’s Hospital, patientAgeRange, 0–18 years]
-
A.
ageRange
chosen
Indicates the span of ages within which an entity or relationship is considered valid or applicable.
-
B.
ageRangeUpper
Indicates the maximum age limit that bounds the upper end of an age range associated with an entity or relationship.
-
C.
hasApproximateAgeRange
Indicates that one entity is associated with another entity representing an estimated or non-exact span of ages.
-
D.
containsAge
Indicates that one entity includes or specifies the age value or age-related information of another entity.
-
E.
ageGroup
Indicates the categorical age range or bracket to which an entity belongs.
- 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_69d8d396f54c8190ba49db31e8743842 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5978154ac819096356d2a488b45f0 |
completed | April 20, 2026, 3:03 a.m. |
| PD | Predicate disambiguation | batch_69e48d16dd34819096e096d0c0e4c15c |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:52 a.m.