Triple
T18204514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ALBERT |
E435869
|
entity |
| Predicate | evaluatedOn |
P82415
|
FINISHED |
| Object | GLUE benchmark |
—
|
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: GLUE benchmark | Statement: [ALBERT, evaluatedOn, GLUE benchmark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GLUE benchmark Context triple: [ALBERT, evaluatedOn, GLUE benchmark]
-
A.
GLUE benchmark
chosen
The GLUE benchmark is a widely used collection of natural language understanding tasks designed to evaluate and compare the performance of language models.
-
B.
GLUE
GLUE is a widely used benchmark suite for evaluating the performance of natural language understanding models across a variety of language tasks.
-
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.
SQuAD 2.0
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.
-
E.
Hugging Face
Hugging Face is an AI company and open-source community best known for its tools and libraries that make it easy to build, share, and deploy state-of-the-art machine learning models.
- 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.