Triple
T20106652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PaLM 2 |
E490194
|
entity |
| Predicate | familyMember |
P566
|
FINISHED |
| Object | PaLM 2 Gecko |
—
|
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: PaLM 2 Gecko | Statement: [PaLM 2, familyMember, PaLM 2 Gecko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PaLM 2 Gecko Context triple: [PaLM 2, familyMember, PaLM 2 Gecko]
-
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-NeoX-20B
GPT-NeoX-20B is a 20-billion-parameter open-source large language model developed by EleutherAI as a powerful successor to the GPT-Neo family for advanced text generation and research.
-
C.
Grok
Grok is an AI chatbot developed by xAI, designed to provide conversational access to real-time information and reasoning capabilities.
-
D.
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.
-
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 (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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e666dcb8d4819091889e19dd9137a6 |
completed | April 20, 2026, 5:48 p.m. |
Created at: April 11, 2026, 11:28 p.m.