Triple
T6517965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Islamic lunar calendar |
E148310
|
entity |
| Predicate | hasMonthLength |
P28022
|
FINISHED |
| Object | 29 or 30 days |
—
|
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: 29 or 30 days | Statement: [Islamic lunar calendar, hasMonthLength, 29 or 30 days]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMonthLength Context triple: [Islamic lunar calendar, hasMonthLength, 29 or 30 days]
-
A.
hasAverageMonthLength
Indicates that an entity is associated with a specified average length of a month, typically expressed in days.
-
B.
hasMonthCount
Indicates a relationship where an entity is associated with a specific number of months.
-
C.
hasMonth
Indicates that something is associated with, occurs in, or is assigned to a specific month.
-
D.
hasNumberOfDaysIn13thMonth
Indicates the specific count of days that occur in the thirteenth month of a given calendar or time system.
-
E.
monthLengthPattern
chosen
Indicates the recurring sequence or pattern of the number of days in the months within a given calendar or time system.
- 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_69c687e68e748190baceb9298f32d3ed |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ac108abc819082d1368af6611a92 |
completed | March 27, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69c68ab98c78819081743e614df04e1d |
completed | March 27, 2026, 1:48 p.m. |
Created at: March 27, 2026, 1:44 p.m.