Triple
T759036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dewey Decimal Classification |
E16023
|
entity |
| Predicate | hasStandardSubdivisionRange |
P19281
|
FINISHED |
| Object | –01 to –09 |
—
|
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: –01 to –09 | Statement: [Dewey Decimal Classification, hasStandardSubdivisionRange, –01 to –09]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStandardSubdivisionRange Context triple: [Dewey Decimal Classification, hasStandardSubdivisionRange, –01 to –09]
-
A.
hasSubdivision
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
-
B.
hasSubregionStatus
Indicates that one region holds an official or defined status as a subregion within a larger geographic or administrative area.
-
C.
hasDivisionLevel
Indicates that one entity is associated with a specific hierarchical or organizational division level of another entity.
-
D.
hasNumberOfDivisions
Indicates the relationship that specifies how many divisions or subunits an entity possesses.
-
E.
hasStandardizedDimensions
Indicates that an entity conforms to a predefined, uniform set of measurements or size specifications.
- 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_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. |
| PDg | Predicate description generation | batch_69a4a62b497081909503c8d30c7ce1db |
completed | March 1, 2026, 8:48 p.m. |
Created at: March 1, 2026, 7:37 p.m.