Triple
T652337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nisan |
E11370
|
entity |
| Predicate | correspondsRoughlyToGregorianMonths |
P16936
|
FINISHED |
| Object | March |
—
|
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: March | Statement: [Nisan, correspondsRoughlyToGregorianMonths, March]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: correspondsRoughlyToGregorianMonths Context triple: [Nisan, correspondsRoughlyToGregorianMonths, March]
-
A.
dateRelativeToGregorian
Indicates how a given date is positioned or expressed in relation to the standard Gregorian calendar date.
-
B.
monthObserved
Indicates the month during which an event, observation, or measurement took place.
-
C.
hasAverageMonthLength
Indicates that an entity is associated with a specified average length of a month, typically expressed in days.
-
D.
hasCommonYearMonthCount
Indicates that two entities share the same number of distinct year–month combinations associated with them.
-
E.
hasMonthCount
Indicates a relationship where an entity is associated with a specific number of months.
- 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.