Triple
T18204855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XLM-R |
E435876
|
entity |
| Predicate | usesPositionalEncoding |
P7030
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [XLM-R, usesPositionalEncoding, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesPositionalEncoding Context triple: [XLM-R, usesPositionalEncoding, true]
-
A.
usesPositionalNotation
Indicates that one entity represents numbers using a positional numeral system, where a digit’s value depends on its position.
-
B.
embeddingType
chosen
Indicates the specific kind or category of embedding representation used to encode an entity or data.
-
C.
advantageOverAutoregressiveModels
Indicates that one method, system, or approach possesses benefits or superior performance compared to autoregressive models.
-
D.
usesLossFunction
Indicates that one entity employs a particular loss function as part of its optimization or learning process.
-
E.
encodedIn
Indicates that one entity is represented, stored, or expressed within another entity using a specific encoding or format.
- 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:32 a.m.