Triple

T8415092
Position Surface form Disambiguated ID Type / Status
Subject Core ML E198712 entity
Predicate integratesWith P1075 FINISHED
Object BNNS
BNNS (Basic Neural Network Subroutines) is Apple’s low-level, hardware-accelerated framework for performing neural network and machine learning computations efficiently on Apple devices.
 E732975 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: BNNS | Statement: [Core ML, integratesWith, BNNS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BNNS
Context triple: [Core ML, integratesWith, BNNS]
  • A. .bn
    .bn is the country code top-level domain (ccTLD) assigned to Brunei Darussalam for use in its internet addresses.
  • B. SNN
    SNN is the National Rail station code assigned to Swinton railway station in South Yorkshire, England.
  • C. SNN
    SNN is the three-letter IATA airport code for Shannon Airport in County Clare, Ireland, a major international gateway on the country’s west coast.
  • D. NNS
    NNS is the commonly used abbreviation for Newport News Shipbuilding, a major American shipyard known for constructing U.S. Navy aircraft carriers and submarines.
  • E. BN2
    BN2 is a UK postal district covering parts of eastern Brighton and nearby areas within the BN (Brighton) postcode region.
  • 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: BNNS
Triple: [Core ML, integratesWith, BNNS]
Generated description
BNNS (Basic Neural Network Subroutines) is Apple’s low-level, hardware-accelerated framework for performing neural network and machine learning computations efficiently on Apple devices.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: BNNS
Target entity description: BNNS (Basic Neural Network Subroutines) is Apple’s low-level, hardware-accelerated framework for performing neural network and machine learning computations efficiently on Apple devices.
  • A. .bn
    .bn is the country code top-level domain (ccTLD) assigned to Brunei Darussalam for use in its internet addresses.
  • B. SNN
    SNN is the National Rail station code assigned to Swinton railway station in South Yorkshire, England.
  • C. SNN
    SNN is the three-letter IATA airport code for Shannon Airport in County Clare, Ireland, a major international gateway on the country’s west coast.
  • D. NNS
    NNS is the commonly used abbreviation for Newport News Shipbuilding, a major American shipyard known for constructing U.S. Navy aircraft carriers and submarines.
  • E. BN2
    BN2 is a UK postal district covering parts of eastern Brighton and nearby areas within the BN (Brighton) postcode region.
  • 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_69ca831201b481909e137936ef99ff11 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cb83e443a08190983d9a0a61e0f781 completed March 31, 2026, 8:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce032a25ec819094c6346eb2a7f973 completed April 2, 2026, 5:48 a.m.
NEDg Description generation batch_69ce0781859c8190bb92f41c00af459b completed April 2, 2026, 6:06 a.m.
NED2 Entity disambiguation (via description) batch_69ce089d09c08190ba321aed4044a862 completed April 2, 2026, 6:11 a.m.
Created at: March 30, 2026, 6:06 p.m.