Triple
T3590394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Republican Calendar |
E76011
|
entity |
| Predicate | additionalDaysPerYear |
P49552
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [French Republican Calendar, additionalDaysPerYear, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: additionalDaysPerYear Context triple: [French Republican Calendar, additionalDaysPerYear, 5]
-
A.
hasAverageYearLength
Indicates that one entity has a specified average duration for its year (orbital period), typically measured over time.
-
B.
observanceMayVaryByYear
Indicates that the way an observance is recognized, scheduled, or practiced can differ from one year to another.
-
C.
hasDayCountCommonYear
Indicates that something has a specified number of days as it occurs in a common (non-leap) year.
-
D.
hasDays
Indicates that an entity is associated with, spans, or occurs on specific days.
-
E.
possibleLengthDays
Indicates that an entity can have a duration, measured in whole or fractional days, equal to the specified value.
- 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.