Triple
T360424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Insurance Act 1911 |
E7837
|
entity |
| Predicate | ageRangeForCoverage |
P2736
|
FINISHED |
| Object | 16 to 70 |
—
|
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: 16 to 70 | Statement: [National Insurance Act 1911, ageRangeForCoverage, 16 to 70]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ageRangeForCoverage Context triple: [National Insurance Act 1911, ageRangeForCoverage, 16 to 70]
-
A.
ageRange
chosen
Indicates the span of ages within which an entity or relationship is considered valid or applicable.
-
B.
servesAgeRange
Indicates that a service, product, or offering is intended for or applicable to entities within a specified age range.
-
C.
hasCoverage
Indicates that one entity provides insurance or protection coverage for another entity or subject.
-
D.
hasAge
Indicates that an entity possesses a specific age value, typically expressed as a number of time units since its birth or creation.
-
E.
timePeriodCoveredTo
Indicates the span or duration of time that is encompassed, addressed, or relevant to a given subject or entity.
- 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebccb8d88190a31f7c443a0c8566 |
completed | Feb. 28, 2026, 1:21 p.m. |
| PD | Predicate disambiguation | batch_69a2e95aeed48190b5e48865cc964938 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.