Triple
T34908461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Africa/Kinshasa |
E1006795
|
entity |
| Predicate | timeZoneOffsetLabel |
P34679
|
FINISHED |
| Object | UTC+1 |
—
|
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: UTC+1 | Statement: [Africa/Kinshasa, timeZoneOffsetLabel, UTC+1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeZoneOffsetLabel Context triple: [Africa/Kinshasa, timeZoneOffsetLabel, UTC+1]
-
A.
utcOffsetDescription
chosen
Indicates the textual description of how far a time zone’s local time differs from Coordinated Universal Time (UTC), often including details like hours, minutes, and daylight saving adjustments.
-
B.
timeOffsetInHours
Indicates the temporal difference between two time points or events, measured in hours.
-
C.
timeOffsetType
Indicates the type or category of temporal offset that specifies how one time point is shifted relative to another.
-
D.
timeZoneOffsetDestination
Indicates the time difference (offset from a reference time, typically UTC) that applies to the destination location in a temporal or travel-related context.
-
E.
timeOffsetReference
Indicates that a temporal value is specified relative to a particular reference time or event, defining the offset between them.
- 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_69f76dc1b4a081909b4c6e4d8ec0aa2d |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4 p.m.