Triple
T849005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT-2 |
E18339
|
entity |
| Predicate | predecessor |
P97
|
FINISHED |
| Object | GPT |
E18819
|
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: GPT | Statement: [GPT-2, predecessor, GPT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GPT Context triple: [GPT-2, predecessor, GPT]
-
A.
ChatGPT
ChatGPT is an advanced conversational AI model developed by OpenAI that can understand and generate human-like text across a wide range of topics and tasks.
-
B.
GPT-3.5
GPT-3.5 is a large language model that generates human-like text and powers conversational AI applications such as advanced chatbots and coding assistants.
-
C.
GPT-3
chosen
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.
-
D.
GPT-2
GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
-
E.
GPT-4
GPT-4 is a large multimodal language model known for its advanced reasoning, comprehension, and generation capabilities across text and images.
- 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac1fac3481909cba7070ce31a9b3 |
completed | March 1, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3bd8b588190b7a9eb72dce93d07 |
completed | March 4, 2026, 3:15 a.m. |
Created at: March 1, 2026, 7:38 p.m.