Triple

T19411532
Position Surface form Disambiguated ID Type / Status
Subject Dalgona challenge E485597 entity
Predicate relatedTo P37 FINISHED
Object dalgona coffee NE NERFINISHED

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: dalgona coffee | Statement: [Dalgona challenge, relatedTo, dalgona coffee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: dalgona coffee
Context triple: [Dalgona challenge, relatedTo, dalgona coffee]
  • A. Dalgona challenge
    The Dalgona challenge is a viral game popularized by the series "Squid Game," in which participants must carefully cut out a shape from a thin Korean honeycomb candy without breaking it.
  • B. Café au Lait
    Café au Lait is one of the short, conversational vignettes in Jim Jarmusch’s film "Coffee and Cigarettes," featuring characters chatting over coffee in a minimalist, black-and-white setting.
  • C. Koffee
    Koffee is a Jamaican reggae and dancehall singer, songwriter, and rapper known for her Grammy-winning EP "Rapture" and hit single "Toast."
  • D. Korean dalgona candy chosen
    Korean dalgona candy is a traditional Korean street sweet made from melted sugar and baking soda, known for its light, honeycomb-like texture and often stamped shapes.
  • E. Caffe
    Caffe is an open-source deep learning framework known for its speed and modular design, widely used in computer vision research and applications.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8d5162481909db12435d9535c1a completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e62af681288190ba2ec52d5adb6a22 completed April 20, 2026, 1:32 p.m.
Created at: April 10, 2026, 1:37 p.m.