Triple
T23778390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Difference Engine No. 2 |
E587740
|
entity |
| Predicate | maximumDigitsPerNumber |
P11851
|
FINISHED |
| Object | 31 |
—
|
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: 31 | Statement: [Difference Engine No. 2, maximumDigitsPerNumber, 31]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumDigitsPerNumber Context triple: [Difference Engine No. 2, maximumDigitsPerNumber, 31]
-
A.
maximumNumber
Indicates that one entity specifies the highest allowable or observed quantity, value, or count associated with another entity.
-
B.
maximumNumberOfSegments
Indicates the greatest allowable or observed count of discrete segments into which something can be or is divided.
-
C.
hasDigitCount
chosen
Indicates that one entity has a total number of digits equal to the value specified by the other entity.
-
D.
maximumTermCount
Indicates the highest number of terms that are allowed or considered within a given context or operation.
-
E.
maximumConsecutiveTerms
Indicates the greatest number of terms that can occur in an unbroken, continuous sequence within a given context or structure.
- 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_69e2490d245881909028226a1393d624 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1c629f0c08190baccce71ebc72650 |
completed | April 29, 2026, 8:49 a.m. |
| PD | Predicate disambiguation | batch_69f155f79e34819080f9ddb972b34deb |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:16 p.m.