Triple
T759006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dewey Decimal Classification |
E16023
|
entity |
| Predicate | mainSubjectClassCount |
P8911
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [Dewey Decimal Classification, mainSubjectClassCount, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainSubjectClassCount Context triple: [Dewey Decimal Classification, mainSubjectClassCount, 10]
-
A.
numberOfGroups
chosen
Indicates the total count of distinct groups associated with or formed within a given context or entity.
-
B.
hasGradeCount
Indicates a relationship where an entity is associated with the number of grades it has or has received.
-
C.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
-
D.
mathematicalSubjectClassification
Indicates that one entity classifies the mathematical subject area or field to which another entity (such as a work, concept, or topic) belongs.
-
E.
subjectMatter
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
- 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_69a493684ee48190bd43b7c78da4aec8 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a67f9778819098d3c144dd26b976 |
completed | March 1, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69a4a50348088190873a1446db657a78 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.