Triple

T38556456
Position Surface form Disambiguated ID Type / Status
Subject ARM big.LITTLE heterogeneous computing E925257 entity
Predicate usesCoreType P18973 FINISHED
Object ARM Cortex-A15
ARM Cortex-A15 is a high-performance 32-bit ARM processor core designed for mobile and embedded systems, emphasizing strong computational power and support for advanced operating systems like Linux and Android.
 E2274109 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: ARM Cortex-A15 | Statement: [ARM big.LITTLE heterogeneous computing, usesCoreType, ARM Cortex-A15]
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: ARM Cortex-A15
Triple: [ARM big.LITTLE heterogeneous computing, usesCoreType, ARM Cortex-A15]
Generated description
ARM Cortex-A15 is a high-performance 32-bit ARM processor core designed for mobile and embedded systems, emphasizing strong computational power and support for advanced operating systems like Linux and Android.

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_69f76eaeb69c8190b367df9330d6f6af completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fe92879678819096de6cc1353d14ba completed May 9, 2026, 1:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a41e0418274819088dfdc4c8c0f7190 completed June 29, 2026, 3:02 a.m.
NEDg Description generation batch_6a41e0f0242c81909f8320932f505aca completed June 29, 2026, 3:05 a.m.
NED2 Entity disambiguation (via description) batch_6a41e14b84e881908f0c039d5101b5e5 completed June 29, 2026, 3:06 a.m.
Created at: May 3, 2026, 4:32 p.m.