Triple

T7279502
Position Surface form Disambiguated ID Type / Status
Subject Tiger Lake E163110 entity
Predicate supports P516 FINISHED
Object DL Boost
DL Boost is Intel’s deep learning acceleration technology that enhances AI inference performance on its processors through specialized instruction set extensions.
 E653484 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: DL Boost | Statement: [Tiger Lake, supports, DL Boost]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DL Boost
Context triple: [Tiger Lake, supports, DL Boost]
  • A. STL
    STL is a common abbreviation and nickname for the city of St. Louis, Missouri.
  • B. TBB
    TBB is the vehicle registration code used on license plates for the German town of Bad Mergentheim and its surrounding district.
  • C. C++
    C++ is a high-performance, general-purpose programming language widely used for system/software development, game engines, and performance-critical applications.
  • D. BPF
    BPF is the abbreviation for the British Pacific Fleet, a major Royal Navy formation that operated in the Pacific theater during the final stages of World War II.
  • E. BDU
    BDU is a military uniform type commonly associated with the standard camouflage combat attire used by the U.S. armed forces from the early 1980s through the mid-2000s.
  • 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: DL Boost
Triple: [Tiger Lake, supports, DL Boost]
Generated description
DL Boost is Intel’s deep learning acceleration technology that enhances AI inference performance on its processors through specialized instruction set extensions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DL Boost
Target entity description: DL Boost is Intel’s deep learning acceleration technology that enhances AI inference performance on its processors through specialized instruction set extensions.
  • A. STL
    STL is a common abbreviation and nickname for the city of St. Louis, Missouri.
  • B. TBB
    TBB is the vehicle registration code used on license plates for the German town of Bad Mergentheim and its surrounding district.
  • C. C++
    C++ is a high-performance, general-purpose programming language widely used for system/software development, game engines, and performance-critical applications.
  • D. BPF
    BPF is the abbreviation for the British Pacific Fleet, a major Royal Navy formation that operated in the Pacific theater during the final stages of World War II.
  • E. BDU
    BDU is a military uniform type commonly associated with the standard camouflage combat attire used by the U.S. armed forces from the early 1980s through the mid-2000s.
  • F. None of above. chosen

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_69c6885c5964819085b209701769877f completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6eb339b1081909f648864e210f98e completed March 27, 2026, 8:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7db3450208190b67e4329a531ad0c completed March 28, 2026, 1:44 p.m.
NEDg Description generation batch_69c7dc567004819089c6c4b5322f275f completed March 28, 2026, 1:49 p.m.
NED2 Entity disambiguation (via description) batch_69c7dd18f8d481908bd7ac86e4388ce5 completed March 28, 2026, 1:52 p.m.
Created at: March 27, 2026, 2:59 p.m.