Triple
T871340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT-3 |
E18819
|
entity |
| Predicate | modelSize |
P20680
|
FINISHED |
| Object | 175 billion parameters |
—
|
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: 175 billion parameters | Statement: [GPT-3, modelSize, 175 billion parameters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modelSize Context triple: [GPT-3, modelSize, 175 billion parameters]
-
A.
modelNumber
Indicates that one entity is the specific model identifier or code assigned to another entity (such as a product or device).
-
B.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
C.
stateSize
Indicates the relative or absolute physical extent or dimensions of a state, such as its area or population size.
-
D.
bodySize
Indicates the relative physical magnitude or scale of an entity’s body, such as how large or small it is.
-
E.
includesSizeRange
Indicates that one entity specifies or covers a particular range of sizes associated with another entity.
- 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.