Triple
T29856286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tibetan lunar calendar |
E758196
|
entity |
| Predicate | hasApproximateLengthOfYear |
P9857
|
FINISHED |
| Object | about 365 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: about 365 days | Statement: [Tibetan lunar calendar, hasApproximateLengthOfYear, about 365 days]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateLengthOfYear Context triple: [Tibetan lunar calendar, hasApproximateLengthOfYear, about 365 days]
-
A.
approximateTimeInYear
Indicates that one time-related entity represents an estimated or non-exact point or interval within a given year for another entity.
-
B.
hasAverageYearLength
chosen
Indicates that one entity has a specified average duration for its year (orbital period), typically measured over time.
-
C.
hasAverageMonthLength
Indicates that an entity is associated with a specified average length of a month, typically expressed in days.
-
D.
hasApproximateDuration
Indicates that one entity has a duration that is estimated or not exact, typically expressed as an approximate length of time.
-
E.
hasCommonYearMonthCount
Indicates that two entities share the same number of distinct year–month combinations associated with them.
- 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_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8cec6d48190bebfa884b2f938c0 |
completed | May 3, 2026, 7:58 p.m. |
Created at: April 29, 2026, 5:46 p.m.