Triple
T659939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Architectural Work of Le Corbusier, an Outstanding Contribution to the Modern Movement |
E11730
|
entity |
| Predicate | numberOfComponents |
P5741
|
FINISHED |
| Object | 17 |
—
|
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: 17 | Statement: [The Architectural Work of Le Corbusier, an Outstanding Contribution to the Modern Movement, numberOfComponents, 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfComponents Context triple: [The Architectural Work of Le Corbusier, an Outstanding Contribution to the Modern Movement, numberOfComponents, 17]
-
A.
dimensionOfComponents
Indicates that a specified dimension value is associated with, or applies to, the components of an object or system.
-
B.
numberOfConstituents
chosen
Indicates the total count of individual components or members that make up a larger whole or group.
-
C.
numberOfColumns
Indicates the total count of vertical divisions (columns) associated with or contained in a given structure or dataset.
-
D.
numberOfChildren
Indicates the total count of children that an entity has.
-
E.
numberOfCells
Indicates the total count of individual cells associated with or contained in a given entity.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0f55f7481909e052a25bd12d455 |
completed | March 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69a49d1406ec8190abf546549264c85d |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.