Triple
T18204560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DeBERTa |
E435870
|
entity |
| Predicate | benchmark |
P17142
|
FINISHED |
| Object | SQuAD |
—
|
NE NERFINISHED |
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: SQuAD | Statement: [DeBERTa, benchmark, SQuAD]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SQuAD Context triple: [DeBERTa, benchmark, SQuAD]
-
A.
SQuAD 2.0
chosen
SQuAD 2.0 is a widely used reading comprehension benchmark dataset that tests machine learning models’ ability to answer questions from passages while also handling unanswerable queries.
-
B.
MRPC
MRPC is the commonly used abbreviation for the Model Rules of Professional Conduct, a set of ethical standards governing lawyers’ professional behavior in the United States.
-
C.
SuperGLUE
SuperGLUE is a challenging benchmark suite of diverse natural language understanding tasks designed to evaluate and compare the performance of advanced language models.
-
D.
GLUE benchmark
The GLUE benchmark is a widely used collection of natural language understanding tasks designed to evaluate and compare the performance of language models.
-
E.
NLI
NLI is the vehicle registration code used on license plates for cars registered in Orneta, Poland.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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. |
Created at: April 10, 2026, 10:32 a.m.