Triple
T5052872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anthropic Claude |
E113826
|
entity |
| Predicate | hasVersion |
P455
|
FINISHED |
| Object | Claude 3 Haiku |
E113826
|
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: Claude 3 Haiku | Statement: [Anthropic Claude, hasVersion, Claude 3 Haiku]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Claude 3 Haiku Context triple: [Anthropic Claude, hasVersion, Claude 3 Haiku]
-
A.
Anthropic Claude
chosen
Anthropic Claude is an advanced AI assistant developed by Anthropic, designed to provide helpful, honest, and safe natural language interactions.
-
B.
Claude
Claude is a given name most famously associated with Claude Shannon, the American mathematician and electrical engineer known as the father of information theory.
-
C.
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.
-
D.
OpenAI Chat Completions API
The OpenAI Chat Completions API is a cloud-based interface that lets developers integrate advanced conversational AI models into their applications for tasks like dialogue, assistance, and content generation.
-
E.
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.
- 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_69bd443aa1f88190abb992d138f2cf42 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7428d7a88190b990aedae390acbe |
completed | March 20, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bea486b394819082ea80694843b29e |
completed | March 21, 2026, 2 p.m. |
Created at: March 20, 2026, 1:38 p.m.