Triple
T3590392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Republican Calendar |
E76011
|
entity |
| Predicate | monthsPerYear |
P9860
|
FINISHED |
| Object | 12 |
—
|
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 | Statement: [French Republican Calendar, monthsPerYear, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: monthsPerYear Context triple: [French Republican Calendar, monthsPerYear, 12]
-
A.
hasMonthCount
chosen
Indicates a relationship where an entity is associated with a specific number of months.
-
B.
monthObserved
Indicates the month during which an event, observation, or measurement took place.
-
C.
hasAverageMonthLength
Indicates that an entity is associated with a specified average length of a month, typically expressed in days.
-
D.
monthLengthPattern
Indicates the recurring sequence or pattern of the number of days in the months within a given calendar or time system.
-
E.
monthNumber
Indicates the numerical position of a month within a calendar year (e.g., January = 1, February = 2, etc.).
- 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_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. |
Created at: March 8, 2026, 3:22 p.m.