Triple

T20106621
Position Surface form Disambiguated ID Type / Status
Subject PaLM 2 E490194 entity
Predicate follows P134 FINISHED
Object PaLM
PaLM is a large-scale language model developed by Google to perform advanced natural language understanding and generation tasks.
E490194 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: PaLM | Statement: [PaLM 2, follows, PaLM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PaLM
Context triple: [PaLM 2, follows, PaLM]
  • A. PaLM 2
    PaLM 2 is a large-scale language model developed by Google, known for powering various AI features across Google products before being succeeded by the Gemini family of models.
  • B. 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.
  • C. 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.
  • D. GPT-Neo
    GPT-Neo is an open-source family of autoregressive language models developed by EleutherAI as a free alternative to OpenAI’s GPT-3.
  • E. 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.
  • 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: PaLM
Triple: [PaLM 2, follows, PaLM]
Generated description
PaLM is a large-scale language model developed by Google to perform advanced natural language understanding and generation tasks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: PaLM
Target entity description: PaLM is a large-scale language model developed by Google to perform advanced natural language understanding and generation tasks.
  • A. PaLM 2 chosen
    PaLM 2 is a large-scale language model developed by Google, known for powering various AI features across Google products before being succeeded by the Gemini family of models.
  • B. 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.
  • C. 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.
  • D. GPT-Neo
    GPT-Neo is an open-source family of autoregressive language models developed by EleutherAI as a free alternative to OpenAI’s GPT-3.
  • E. 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.
  • F. None of above.

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_69da62636cc08190982cc71733a17b8d completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e666dcb8d4819091889e19dd9137a6 completed April 20, 2026, 5:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a082714d6648190bfc4c3ba92b8bd45 completed May 16, 2026, 8:13 a.m.
NEDg Description generation batch_6a082915c66881909dadac08ea4b8bd3 completed May 16, 2026, 8:21 a.m.
NED2 Entity disambiguation (via description) batch_6a0829a921688190a52538f7537e4ce5 completed May 16, 2026, 8:24 a.m.
Created at: April 11, 2026, 11:28 p.m.