Triple
T31428953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AFT |
E801739
|
entity |
| Predicate | timeFormatExample |
P2162
|
FINISHED |
| Object | 12:00 UTC = 16:30 AFT |
—
|
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: 12:00 UTC = 16:30 AFT | Statement: [AFT, timeFormatExample, 12:00 UTC = 16:30 AFT]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeFormatExample Context triple: [AFT, timeFormatExample, 12:00 UTC = 16:30 AFT]
-
A.
timeNotation
chosen
Indicates the specific system or format used to represent and write times (e.g., 12-hour vs 24-hour notation).
-
B.
timeStructure
Indicates that one entity defines, constrains, or organizes the temporal framework or schedule within which another entity exists or operates.
-
C.
formatDSTExample
Indicates that an example is provided to illustrate how something should be formatted in a dialog state tracking (DST) context.
-
D.
timeType
Indicates the specific temporal category or classification associated with a time-related entity or value (e.g., duration, point in time, interval, or recurrence type).
-
E.
timeToSentence
Indicates the amount of time that elapses from a relevant starting point (e.g., offense, arrest, or charge) until a formal sentence is imposed.
- 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_69f348c475348190bf579ca858eec77c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a5f71b2c8190aade8a83f465be0c |
completed | May 3, 2026, 1:33 a.m. |
| PD | Predicate disambiguation | batch_69f69fe66df08190958558d63ee623d9 |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 30, 2026, 8:55 p.m.