Triple
T582202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kislev |
E15081
|
entity |
| Predicate | hasNumberInSequence |
P14612
|
FINISHED |
| Object | 3 (civil year count) |
—
|
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: 3 (civil year count) | Statement: [Kislev, hasNumberInSequence, 3 (civil year count)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberInSequence Context triple: [Kislev, hasNumberInSequence, 3 (civil year count)]
-
A.
hasNumberInYear
Indicates that a specific number is associated with or occurs within a given year.
-
B.
hasDigitCount
Indicates that one entity has a total number of digits equal to the value specified by the other entity.
-
C.
hasNumberCategory
Indicates that an entity is associated with a specific numerical classification or type.
-
D.
hasOrdinalSeries
chosen
Indicates that one entity belongs to, or is positioned within, an ordered sequence or series relative to other entities.
-
E.
isSectionNumber
Indicates that one entity is the section number identifier associated with another entity, typically within a structured document or text.
- 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_69a4935783b8819082b77726ec10cc42 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49b85becc8190b4d98c00e5fa7c04 |
completed | March 1, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69a494c7f9008190bd8d05b4dc2a7c7f |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.