Triple
T16542474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Europe/Warsaw |
E401852
|
entity |
| Predicate | typicalUtcOffsetRange |
P123966
|
FINISHED |
| Object | +01:00 to +02:00 |
—
|
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: +01:00 to +02:00 | Statement: [Europe/Warsaw, typicalUtcOffsetRange, +01:00 to +02:00]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalUtcOffsetRange Context triple: [Europe/Warsaw, typicalUtcOffsetRange, +01:00 to +02:00]
-
A.
typicalOffsetRange
Indicates the usual or expected range of positional or temporal deviation between related elements.
-
B.
typicalOffsetFromCentralTime
Indicates the usual time difference between a given time zone and Central Time (CT), expressed as an offset in hours or minutes.
-
C.
typicalOffsetFromPacificTime
Indicates the usual time difference between a given time zone and Pacific Time (PT), without accounting for temporary variations like daylight saving changes.
-
D.
typicalOffsetWinter
Indicates the usual temporal or spatial offset associated with winter in relation to a reference point or period.
-
E.
timeOffsetInHours
Indicates the temporal difference between two time points or events, measured in hours.
- F. None of above. chosen
Provenance (4 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3455db6788190b929546050ea2488 |
completed | April 18, 2026, 8:48 a.m. |
| PD | Predicate disambiguation | batch_69e2969fab208190ad64164d24748c45 |
completed | April 17, 2026, 8:22 p.m. |
| PDg | Predicate description generation | batch_69e2d7f97e548190a474691a152bd8e8 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:15 a.m.