Triple
T19190089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT-1 |
E469810
|
entity |
| Predicate | numberOfParameters |
P21071
|
FINISHED |
| Object | 117M |
—
|
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: 117M | Statement: [GPT-1, numberOfParameters, 117M]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfParameters Context triple: [GPT-1, numberOfParameters, 117M]
-
A.
parameterCount
chosen
Indicates the number of parameters associated with a given function, method, or callable entity.
-
B.
parameterCountRelativeTo
Indicates a relationship comparing the number of parameters of one entity (such as a function, method, or operation) to that of another entity.
-
C.
numberOfParametersOfLargestVariant
Indicates the total count of parameters in the variant that has the greatest number of parameters among all variants of an entity.
-
D.
argumentCount
Indicates the number of arguments that a function, method, or callable entity takes.
-
E.
numberOfUnknowns
Indicates the count of variables or elements in a situation, equation, or problem whose values are not yet determined or specified.
- 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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f8a16e20819080baa5112f000b41 |
completed | April 20, 2026, 9:57 a.m. |
| PD | Predicate disambiguation | batch_69e4b9bb158481909478ca2e06f3ba39 |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 12:07 p.m.