Triple
T29856287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tibetan lunar calendar |
E758196
|
entity |
| Predicate | hasNumberOfMonthsPerYear |
P9860
|
FINISHED |
| Object | 12 or 13 |
—
|
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 or 13 | Statement: [Tibetan lunar calendar, hasNumberOfMonthsPerYear, 12 or 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfMonthsPerYear Context triple: [Tibetan lunar calendar, hasNumberOfMonthsPerYear, 12 or 13]
-
A.
hasMonthCount
chosen
Indicates a relationship where an entity is associated with a specific number of months.
-
B.
hasAverageMonthLength
Indicates that an entity is associated with a specified average length of a month, typically expressed in days.
-
C.
hasNumberOfDaysIn13thMonth
Indicates the specific count of days that occur in the thirteenth month of a given calendar or time system.
-
D.
hasCommonYearMonthCount
Indicates that two entities share the same number of distinct year–month combinations associated with them.
-
E.
maximumNumberOfMonthsPerYearWithIntercalation
Indicates the greatest number of months that can occur in a single year when intercalary (extra) months are added.
- 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_69f2245a82cc8190a387e7d0118d710b |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
Created at: April 29, 2026, 5:46 p.m.