Triple
T871365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT-3 |
E18819
|
entity |
| Predicate | numberOfParametersOfLargestVariant |
P20681
|
FINISHED |
| Object | 175B |
—
|
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: 175B | Statement: [GPT-3, numberOfParametersOfLargestVariant, 175B]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfParametersOfLargestVariant Context triple: [GPT-3, numberOfParametersOfLargestVariant, 175B]
-
A.
isLargestOf
Indicates that one entity has the greatest size, extent, or magnitude among a specified set of entities.
-
B.
numberOfPositions
Indicates the total count of distinct positions or roles associated with a given entity.
-
C.
numberOfVolumes
Indicates the total count of separate volumes or parts that make up a multi-volume work or collection.
-
D.
numberOfColumns
Indicates the total count of vertical divisions (columns) associated with or contained in a given structure or dataset.
-
E.
dimensionOfComponents
Indicates that a specified dimension value is associated with, or applies to, the components of an object or system.
- 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_69a4938db1f081909bcd1ad2713b6096 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac96850881908a2d776685126137 |
completed | March 1, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69a4aa89ca008190b50d061ac7fe19f9 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4a38ec8190915916d80299ab55 |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:39 p.m.