Triple
T32152324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barker balancing test |
E821191
|
entity |
| Predicate | factorCount |
P114017
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Barker balancing test, factorCount, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: factorCount Context triple: [Barker balancing test, factorCount, 4]
-
A.
hasDivisorsCount
chosen
Indicates that an entity (typically a number) is associated with the specific count of its divisors.
-
B.
primeFactorization
Indicates that one entity is the decomposition of another entity into a multiset or sequence of prime factors whose product equals the original.
-
C.
factor
Indicates that one entity is a contributing cause, influence, or component affecting the state, outcome, or existence of another entity.
-
D.
bitLengthFactorization
Indicates a relationship where the bit-length of a number is determined or constrained by the factorization of that number.
-
E.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
- 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_69f3490520d081909b2f1271dab75faa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b9eccab88190a2895cbb0332c5a7 |
completed | May 3, 2026, 2:58 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a970b0819090c6473844ffa8e3 |
completed | May 3, 2026, 2:32 a.m. |
Created at: May 1, 2026, 12:31 a.m.