Triple
T205344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | World Health Statistics |
E4598
|
entity |
| Predicate | hasTemporalCoverage |
P3058
|
FINISHED |
| Object | multiple years of historical data |
—
|
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: multiple years of historical data | Statement: [World Health Statistics, hasTemporalCoverage, multiple years of historical data]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTemporalCoverage Context triple: [World Health Statistics, hasTemporalCoverage, multiple years of historical data]
-
A.
timePeriodCoveredTo
chosen
Indicates the span or duration of time that is encompassed, addressed, or relevant to a given subject or entity.
-
B.
historicallyRecordedSince
Indicates that the existence, occurrence, or recognition of one entity has been documented in historical records starting from the time specified by another entity.
-
C.
hasLongTermDatasetSince
Indicates that an entity has maintained or used a particular dataset continuously starting from a specified point in time.
-
D.
hasLandCoverage
Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
-
E.
hasCoverage
Indicates that one entity provides insurance or protection coverage for another entity or subject.
- 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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25f46b4f081909e5ee3718109a71f |
completed | Feb. 28, 2026, 3:21 a.m. |
| PD | Predicate disambiguation | batch_69a25b4b42ec8190bef16bbbdd30a742 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:51 a.m.