Triple
T21344434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel M. Ziegler |
E526291
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | OpenAI |
—
|
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: OpenAI | Statement: [Daniel M. Ziegler, employer, OpenAI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OpenAI Context triple: [Daniel M. Ziegler, employer, OpenAI]
-
A.
OpenAI
chosen
OpenAI is an artificial intelligence research organization best known for developing advanced AI models such as ChatGPT and GPT series.
-
B.
EleutherAI
EleutherAI is an open-source research collective focused on developing and releasing large language models and related tools to advance accessible AI research.
-
C.
Cohere
Cohere is an artificial intelligence company known for developing large language models and NLP platforms for enterprises.
-
D.
Meta AI
Meta AI is Meta Platforms’ artificial intelligence division, responsible for developing large-scale AI models, research, and consumer-facing tools like the Meta AI assistant integrated across its apps and services.
-
E.
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.
- 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_69e0b51c33048190ab27cede74ef798c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8a85274f481909e699b390bed9350 |
completed | April 22, 2026, 10:52 a.m. |
Created at: April 16, 2026, 4:44 p.m.