Triple

T1775202
Position Surface form Disambiguated ID Type / Status
Subject Apple Neural Engine E38961 entity
Predicate alsoKnownAs P39 FINISHED
Object ANE
ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
 E198710 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: ANE | Statement: [Apple Neural Engine, alsoKnownAs, ANE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ANE
Context triple: [Apple Neural Engine, alsoKnownAs, ANE]
  • A. ANA
    ANA is the commonly used abbreviation for the Afghan National Army, the former main land warfare branch of Afghanistan’s armed forces.
  • B. ANA
    ANA is the standard three-letter abbreviation used for the Anaheim Ducks, a professional ice hockey team in the National Hockey League.
  • C. AN
    AN is the vehicle registration code used on license plates for the Ansbach district in the Middle Franconia region of Bavaria, Germany.
  • D. ENA
    ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • E. NA
    NA is the commonly used abbreviation for the National Assembly of Pakistan, the lower house of the country's bicameral parliament.
  • 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: ANE
Triple: [Apple Neural Engine, alsoKnownAs, ANE]
Generated description
ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ANE
Target entity description: ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • A. ANA
    ANA is the commonly used abbreviation for the Afghan National Army, the former main land warfare branch of Afghanistan’s armed forces.
  • B. ANA
    ANA is the standard three-letter abbreviation used for the Anaheim Ducks, a professional ice hockey team in the National Hockey League.
  • C. AN
    AN is the vehicle registration code used on license plates for the Ansbach district in the Middle Franconia region of Bavaria, Germany.
  • D. ENA
    ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • E. NA
    NA is the commonly used abbreviation for the National Assembly of Pakistan, the lower house of the country's bicameral parliament.
  • 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_69a8862e61708190af97b9838cc3f5de completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa64b6c4a88190ab2f75c8d4814f11 completed March 6, 2026, 5:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada9982d208190b0c29ee1141e91b0 completed March 8, 2026, 4:53 p.m.
NEDg Description generation batch_69adab03a5448190b42966adcd8afbde completed March 8, 2026, 4:59 p.m.
NED2 Entity disambiguation (via description) batch_69adaeabff6c8190b19bc6478a28641c completed March 8, 2026, 5:15 p.m.
Created at: March 4, 2026, 7:31 p.m.