Triple

T24720025
Position Surface form Disambiguated ID Type / Status
Subject Kimura two-parameter model E612268 entity
Predicate comparedWith P278 FINISHED
Object HKY85 model
The HKY85 model is a nucleotide substitution model in molecular evolution that extends simpler models by allowing different rates for transitions and transversions as well as unequal base frequencies.
 E1646595 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: HKY85 model | Statement: [Kimura two-parameter model, comparedWith, HKY85 model]
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: HKY85 model
Triple: [Kimura two-parameter model, comparedWith, HKY85 model]
Generated description
The HKY85 model is a nucleotide substitution model in molecular evolution that extends simpler models by allowing different rates for transitions and transversions as well as unequal base frequencies.

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_69e2d7d6e7a48190bb43b0d8bb1137a0 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f410170ab08190ace17c8e705a4b10 completed May 1, 2026, 2:29 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101017caa8819088e8d8e1c4018736 completed May 22, 2026, 8:13 a.m.
NEDg Description generation batch_6a10136d2c448190918a7eeb751a2a84 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10143c5c84819081dd4f953fa9841a completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 3:40 a.m.