Triple
T18258419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Language Models are Unsupervised Multitask Learners |
E437278
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | GPT series |
—
|
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: GPT series | Statement: [Language Models are Unsupervised Multitask Learners, relatedTo, GPT series]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GPT series Context triple: [Language Models are Unsupervised Multitask Learners, relatedTo, GPT series]
-
A.
GPT series
chosen
The GPT series is a family of large language models developed by OpenAI that generate human-like text and perform a wide range of natural language tasks.
-
B.
GPT
GPT is a family of large language models developed by OpenAI that can understand and generate human-like text for a wide range of tasks.
-
C.
GPT
GPT (GUID Partition Table) is a modern disk partitioning scheme that supports large drives, many partitions, and improved reliability compared to older MBR- and APM-based systems.
-
D.
GPT
GPT is the IATA airport code for Gulfport–Biloxi International Airport in Gulfport, Mississippi, United States.
-
E.
GPT-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
- 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_69d8b913351c8190932b6a426de04b41 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4fd8879e88190893f8da7c3529496 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 10:34 a.m.