Triple
T432775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arecibo message |
E9748
|
entity |
| Predicate | bitLengthFactorization |
P13893
|
FINISHED |
| Object | 23 × 73 |
—
|
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: 23 × 73 | Statement: [Arecibo message, bitLengthFactorization, 23 × 73]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bitLengthFactorization Context triple: [Arecibo message, bitLengthFactorization, 23 × 73]
-
A.
bitWidth
Indicates the number of bits used to represent or encode a given value, type, or data element.
-
B.
logarithmBaseDetermines
Indicates that the chosen base of a logarithm determines how a quantity is expressed or scaled in logarithmic terms.
-
C.
isFeatureLength
Indicates that something (typically a film or video) has a duration long enough to be considered a full-length, standard feature.
-
D.
degreeOfDecipherment
Indicates the extent to which something (such as a text, code, or script) has been successfully deciphered or made intelligible.
-
E.
number
Indicates that one entity is associated with a specific numerical value or count in relation to another entity or context.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ef084840819080653004b674cba8 |
completed | Feb. 28, 2026, 1:35 p.m. |
| PD | Predicate disambiguation | batch_69a2edd9264c8190b92f9a50348e5541 |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eeb93584819082f23eff13e17c4f |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.