Triple

T953623
Position Surface form Disambiguated ID Type / Status
Subject Grok E20576 entity
Predicate hasModelVersion P11583 FINISHED
Object Grok-1.5V E20576 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: Grok-1.5V | Statement: [Grok, hasModelVersion, Grok-1.5V]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Grok-1.5V
Context triple: [Grok, hasModelVersion, Grok-1.5V]
  • A. Grok chosen
    Grok is an AI chatbot developed by xAI, designed to provide conversational access to real-time information and reasoning capabilities.
  • 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
    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. 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-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_69a493b0f2fc81908cd227480a5356a1 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b74fc204819083dbed5c19c4bc15 completed March 1, 2026, 10:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac1cd569a4819082687be40145331e completed March 7, 2026, 12:40 p.m.
Created at: March 1, 2026, 7:40 p.m.