Triple
T18204451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BART |
E435868
|
entity |
| Predicate | combinesIdeasFrom |
P94092
|
FINISHED |
| Object | GPT |
—
|
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 | Statement: [BART, combinesIdeasFrom, GPT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GPT Context triple: [BART, combinesIdeasFrom, GPT]
-
A.
GPT
chosen
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.
-
B.
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.
-
C.
GPT
GPT is the IATA airport code for Gulfport–Biloxi International Airport in Gulfport, Mississippi, United States.
-
D.
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.
-
E.
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.
- 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
Created at: April 10, 2026, 10:32 a.m.