Triple
T11241893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maya calendar |
E266090
|
entity |
| Predicate | LongCountNotation |
P71807
|
FINISHED |
| Object | vigesimal positional system |
—
|
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: vigesimal positional system | Statement: [Maya calendar, LongCountNotation, vigesimal positional system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: LongCountNotation Context triple: [Maya calendar, LongCountNotation, vigesimal positional system]
-
A.
offsetNotation
Indicates that one representation specifies how far and in what way another representation is shifted or displaced from a reference point or baseline.
-
B.
usesPositionalNotation
chosen
Indicates that one entity represents numbers using a positional numeral system, where a digit’s value depends on its position.
-
C.
usesCounterLength
Indicates that one entity determines or measures something based on the length of a counter value.
-
D.
laterNumberingSystem
Indicates that one numbering system was adopted or used after another, reflecting a subsequent or more recent scheme of numbering.
-
E.
lengthInWords
Indicates the number of words that make up the length of something, typically a text or expression.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e919eaf48190a1457851cfc56afb |
completed | April 9, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69d7878906f48190b63ddc103a0c8f9b |
completed | April 9, 2026, 11:03 a.m. |
Created at: April 8, 2026, 9:30 p.m.