Triple

T2585508
Position Surface form Disambiguated ID Type / Status
Subject iMac G3 E57187 entity
Predicate colorVariant P60 FINISHED
Object Snow
Snow is a white color variant of the iMac G3, known for its clean, minimalist appearance among the line’s iconic translucent and colorful designs.
 E279568 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: Snow | Statement: [iMac G3, colorVariant, Snow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Snow
Context triple: [iMac G3, colorVariant, Snow]
  • A. Snow
    "Snow" is a political and philosophical novel by Turkish Nobel laureate Orhan Pamuk that explores identity, secularism, and Islamism in contemporary Turkey.
  • B. Winter
    "Winter" is an episode of the science documentary series *Frozen Planet* that explores how animals and ecosystems survive and adapt during the harsh polar winter.
  • C. Frost
    Frost is a common English surname borne by numerous notable individuals, including the American poet Robert Frost.
  • D. Frost
    Frost is the middle name of George F. Kennan, the influential American diplomat and historian known for shaping the U.S. Cold War containment strategy.
  • E. Thunder Snow
    Thunder Snow is a prominent Irish-bred Thoroughbred racehorse best known for winning back-to-back Dubai World Cups in 2018 and 2019.
  • 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: Snow
Triple: [iMac G3, colorVariant, Snow]
Generated description
Snow is a white color variant of the iMac G3, known for its clean, minimalist appearance among the line’s iconic translucent and colorful designs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Snow
Target entity description: Snow is a white color variant of the iMac G3, known for its clean, minimalist appearance among the line’s iconic translucent and colorful designs.
  • A. Snow
    "Snow" is a political and philosophical novel by Turkish Nobel laureate Orhan Pamuk that explores identity, secularism, and Islamism in contemporary Turkey.
  • B. Winter
    "Winter" is an episode of the science documentary series *Frozen Planet* that explores how animals and ecosystems survive and adapt during the harsh polar winter.
  • C. Frost
    Frost is a common English surname borne by numerous notable individuals, including the American poet Robert Frost.
  • D. Frost
    Frost is the middle name of George F. Kennan, the influential American diplomat and historian known for shaping the U.S. Cold War containment strategy.
  • E. Thunder Snow
    Thunder Snow is a prominent Irish-bred Thoroughbred racehorse best known for winning back-to-back Dubai World Cups in 2018 and 2019.
  • 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_69ab4a4dca6481908c301f8e317396e7 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd3cd07588190b3cb8cc348f12938 completed March 7, 2026, 7:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69af6581f6fc819099ea28ecbb0093d7 completed March 10, 2026, 12:27 a.m.
NEDg Description generation batch_69af67bbd6048190aaa091e6ce8cdd23 completed March 10, 2026, 12:37 a.m.
NED2 Entity disambiguation (via description) batch_69af6839cd1c81909ef772cda4bd2f9c completed March 10, 2026, 12:39 a.m.
Created at: March 6, 2026, 9:49 p.m.