Triple
T25366916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | America/Cayenne |
E632824
|
entity |
| Predicate | timeZoneOffsetType |
P4820
|
FINISHED |
| Object | fixed offset |
—
|
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: fixed offset | Statement: [America/Cayenne, timeZoneOffsetType, fixed offset]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeZoneOffsetType Context triple: [America/Cayenne, timeZoneOffsetType, fixed offset]
-
A.
timeOffsetType
Indicates the type or category of temporal offset that specifies how one time point is shifted relative to another.
-
B.
timeZoneType
chosen
Indicates the classification or category of a time zone associated with an entity (e.g., standard, daylight, or specific time zone format/type).
-
C.
timeOffsetInHours
Indicates the temporal difference between two time points or events, measured in hours.
-
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.
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_69e75a90c0dc819092f928b6ea0ecc72 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f4a10eb1748190aa576850282c808d |
completed | May 1, 2026, 12:48 p.m. |
| PD | Predicate disambiguation | batch_69f45d0dbc8c8190beecce679fce90a4 |
completed | May 1, 2026, 7:58 a.m. |
Created at: April 21, 2026, 1:37 p.m.