Triple
T3590432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Republican Calendar |
E76011
|
entity |
| Predicate | minutesPerHourDecimalTime |
P49571
|
FINISHED |
| Object | 100 |
—
|
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: 100 | Statement: [French Republican Calendar, minutesPerHourDecimalTime, 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: minutesPerHourDecimalTime Context triple: [French Republican Calendar, minutesPerHourDecimalTime, 100]
-
A.
timeNotation
Indicates the specific system or format used to represent and write times (e.g., 12-hour vs 24-hour notation).
-
B.
timeEquivalentOf
Indicates that two temporal entities represent the same point in time or duration, possibly expressed in different formats or units.
-
C.
timeStructure
Indicates that one entity defines, constrains, or organizes the temporal framework or schedule within which another entity exists or operates.
-
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.
minutesAheadOf
Indicates that one entity occurs or is positioned a specified number of minutes earlier in time than another entity.
- 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_69ad85d8042081908af94a04c410dec0 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc13c9514819096adf60b15016b8b |
completed | March 8, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69adb839b4e08190b1c0d611cccb11ae |
completed | March 8, 2026, 5:56 p.m. |
| PDg | Predicate description generation | batch_69adb902e61c81908f10494f828e260f |
completed | March 8, 2026, 5:59 p.m. |
Created at: March 8, 2026, 3:22 p.m.