Triple
T652391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metonic cycle |
E11371
|
entity |
| Predicate | totalCommonYears |
P16941
|
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: [Metonic cycle, totalCommonYears, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalCommonYears Context triple: [Metonic cycle, totalCommonYears, 12]
-
A.
countsYearsFrom
Indicates a temporal relationship where the number of years is measured starting from a specified reference point or event.
-
B.
hasCommonYearMonthCount
Indicates that two entities share the same number of distinct year–month combinations associated with them.
-
C.
hasAverageYearLength
Indicates that one entity has a specified average duration for its year (orbital period), typically measured over time.
-
D.
hasNumberInYear
Indicates that a specific number is associated with or occurs within a given year.
-
E.
numberOfCommonUseCharacters
Indicates the count of characters that are shared in common between two entities’ representations or strings.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f35acb08190a3a8248023ce07f9 |
completed | March 1, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69a49d1001088190aa7ca3c8f2ad0e32 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49dc0e6a08190b81d82a6f2571c41 |
completed | March 1, 2026, 8:12 p.m. |
Created at: March 1, 2026, 7:36 p.m.