Triple
T19190093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT-1 |
E469810
|
entity |
| Predicate | publicationVenue |
P309
|
FINISHED |
| Object | OpenAI technical report |
—
|
NE NERFINISHED |
How this triple was built (3 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: OpenAI technical report | Statement: [GPT-1, publicationVenue, OpenAI technical report]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OpenAI technical report Context triple: [GPT-1, publicationVenue, OpenAI technical report]
-
A.
OpenAI
OpenAI is an artificial intelligence research organization best known for developing advanced AI models such as ChatGPT and GPT series.
-
B.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
C.
MIT AI Lab technical report
An MIT AI Lab technical report is a formally published research document produced by the Massachusetts Institute of Technology's Artificial Intelligence Laboratory to disseminate technical findings and developments in AI and related fields.
-
D.
OpenAI API platform
The OpenAI API platform is a cloud-based service that provides developers with programmatic access to OpenAI’s language, code, and other AI models for integration into applications and workflows.
-
E.
ChatGPT Prompt Engineering for Developers
ChatGPT Prompt Engineering for Developers is an online course by DeepLearning.AI that teaches developers practical techniques for crafting effective prompts to build powerful applications with large language models.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: OpenAI technical report Target entity description: The OpenAI technical report is a research publication series by OpenAI that presents and analyzes the development, architecture, and performance of its AI models and related technologies.
-
A.
OpenAI
chosen
OpenAI is an artificial intelligence research organization best known for developing advanced AI models such as ChatGPT and GPT series.
-
B.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
C.
MIT AI Lab technical report
An MIT AI Lab technical report is a formally published research document produced by the Massachusetts Institute of Technology's Artificial Intelligence Laboratory to disseminate technical findings and developments in AI and related fields.
-
D.
OpenAI API platform
The OpenAI API platform is a cloud-based service that provides developers with programmatic access to OpenAI’s language, code, and other AI models for integration into applications and workflows.
-
E.
ChatGPT Prompt Engineering for Developers
ChatGPT Prompt Engineering for Developers is an online course by DeepLearning.AI that teaches developers practical techniques for crafting effective prompts to build powerful applications with large language models.
- F. None of above.
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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f8a16e20819080baa5112f000b41 |
completed | April 20, 2026, 9:57 a.m. |
Created at: April 10, 2026, 12:07 p.m.