Triple
T25421996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saka era |
E637008
|
entity |
| Predicate | monthSystem |
P158285
|
FINISHED |
| Object | solar months in national calendar adaptation |
—
|
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: solar months in national calendar adaptation | Statement: [Saka era, monthSystem, solar months in national calendar adaptation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: monthSystem Context triple: [Saka era, monthSystem, solar months in national calendar adaptation]
-
A.
monthObserved
Indicates the month during which an event, observation, or measurement took place.
-
B.
monthStructure
Indicates how the days within a month are organized, sequenced, or grouped according to a specific calendar or temporal scheme.
-
C.
monthName
Indicates that one entity is the name (in words) of the month represented by the other entity.
-
D.
monthNumber
Indicates the numerical position of a month within a calendar year (e.g., January = 1, February = 2, etc.).
-
E.
monthName2
Indicates that two entities are associated as the same calendar month, where one provides the month’s name and the other represents that month in another form (such as a number or date).
- 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_69e75db4135881909acc287ebcb7a505 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f6bd517c819086c0e8e9c2dce972 |
completed | May 2, 2026, 1:06 p.m. |
| PD | Predicate disambiguation | batch_69f45d0dbc8c8190beecce679fce90a4 |
completed | May 1, 2026, 7:58 a.m. |
| PDg | Predicate description generation | batch_69f464ae42e88190b3549fdf4e0b425e |
completed | May 1, 2026, 8:30 a.m. |
Created at: April 21, 2026, 1:56 p.m.