Triple
T16166342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MJ |
E392310
|
entity |
| Predicate | distinguishedFrom |
P1612
|
FINISHED |
| Object |
LLM
LLM (Large Language Model) is an advanced artificial intelligence system trained on vast text datasets to understand and generate human-like language for a wide range of tasks.
|
E1197463
|
NE FINISHED |
How this triple was built (4 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: LLM | Statement: [MJ, distinguishedFrom, LLM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LLM Context triple: [MJ, distinguishedFrom, LLM]
-
A.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
-
B.
LLaMA
LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
-
C.
Megatron-LM
Megatron-LM is a large-scale language model training framework developed by NVIDIA, designed to efficiently train massive transformer models through model, tensor, and pipeline parallelism.
-
D.
LLM ICL
LLM ICL is a specialized postgraduate law degree focusing on advanced study and practice in international criminal law.
-
E.
AI21 Labs
AI21 Labs is an artificial intelligence company specializing in large language models and advanced natural language processing technologies.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: LLM Triple: [MJ, distinguishedFrom, LLM]
Generated description
LLM (Large Language Model) is an advanced artificial intelligence system trained on vast text datasets to understand and generate human-like language for a wide range of tasks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LLM Target entity description: LLM (Large Language Model) is an advanced artificial intelligence system trained on vast text datasets to understand and generate human-like language for a wide range of tasks.
-
A.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
-
B.
LLaMA
LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
-
C.
Megatron-LM
Megatron-LM is a large-scale language model training framework developed by NVIDIA, designed to efficiently train massive transformer models through model, tensor, and pipeline parallelism.
-
D.
LLM ICL
LLM ICL is a specialized postgraduate law degree focusing on advanced study and practice in international criminal law.
-
E.
AI21 Labs
AI21 Labs is an artificial intelligence company specializing in large language models and advanced natural language processing technologies.
- F. None of above. chosen
Provenance (5 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21eb3ec4c81908d4e5c0f39a85900 |
completed | April 17, 2026, 11:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff7b96bf08190b23bd3b705a34c61 |
completed | May 10, 2026, 3:12 a.m. |
| NEDg | Description generation | batch_69fff87caefc8190836d690dfb2523f9 |
completed | May 10, 2026, 3:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fff98b3d7c8190bb284321d17f58e2 |
completed | May 10, 2026, 3:20 a.m. |
Created at: April 10, 2026, 5:02 a.m.