Triple
T18326058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blissville |
E439010
|
entity |
| Predicate | hasTimezoneOffsetDST |
P44122
|
FINISHED |
| Object | UTC−04:00 |
—
|
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: UTC−04:00 | Statement: [Blissville, hasTimezoneOffsetDST, UTC−04:00]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTimezoneOffsetDST Context triple: [Blissville, hasTimezoneOffsetDST, UTC−04:00]
-
A.
hasTimeOffset
Indicates that one temporal value is shifted or displaced from another by a specified amount of time.
-
B.
observesDSTOffset
chosen
Indicates that one entity follows or applies a specific daylight saving time (DST) offset in its timekeeping or scheduling.
-
C.
hasDst
Indicates that one entity serves as the destination or target location of another entity.
-
D.
DSTOffsetType
Indicates the relationship between a time reference and the amount of time it is shifted from standard time due to daylight saving time adjustments.
-
E.
hasDSTComponent
Indicates that something includes or is associated with a component related to Daylight Saving Time (DST) behavior or configuration.
- 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50aaa66ac819082ec718e8329e0cc |
completed | April 19, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69e44fe4ee10819086b4142444fca1f5 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:36 a.m.