Triple
T1611179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GitHub Copilot |
E34616
|
entity |
| Predicate | uses |
P98
|
FINISHED |
| Object | OpenAI Codex |
E96743
|
NE 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: OpenAI Codex | Statement: [GitHub Copilot, uses, OpenAI Codex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OpenAI Codex Context triple: [GitHub Copilot, uses, OpenAI Codex]
-
A.
OpenAI Codex API
chosen
The OpenAI Codex API is a cloud-based interface that lets developers integrate Codex’s natural-language-to-code generation and code understanding capabilities into their own applications and tools.
-
B.
GitHub Copilot
GitHub Copilot is an AI-powered code completion and pair-programming tool that suggests code and entire functions directly within developers’ editors.
-
C.
OpenAI
OpenAI is an artificial intelligence research organization best known for developing advanced AI models such as ChatGPT and GPT series.
-
D.
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.
-
E.
ChatGPT Enterprise
ChatGPT Enterprise is OpenAI’s business-grade version of ChatGPT, offering enhanced security, admin controls, and scalable access to advanced AI capabilities for organizations.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a885ffc5ec819091afa325d5f9611c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa622b9fbc8190bff82acdde10deb6 |
completed | March 6, 2026, 5:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad51c751308190a89f6462ee365418 |
completed | March 8, 2026, 10:39 a.m. |
Created at: March 4, 2026, 7:28 p.m.