Triple

T18258046
Position Surface form Disambiguated ID Type / Status
Subject Path E437266 entity
Predicate acquiredBy P347 FINISHED
Object Kakao 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: Kakao | Statement: [Path, acquiredBy, Kakao]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kakao
Context triple: [Path, acquiredBy, Kakao]
  • A. Kakao chosen
    Kakao is a South Korean internet and technology company best known for its mobile messaging app KakaoTalk and a wide range of digital services spanning fintech, entertainment, and online platforms.
  • B. Cocoa
    Cocoa is a small city on Florida’s Space Coast known for its historic downtown, proximity to Cape Canaveral, and role as a local educational hub.
  • C. Cocoa
    Cocoa is Apple’s native object-oriented application framework for building graphical user interfaces and other software on macOS.
  • D. Cocoa
    Cocoa is the dried and processed seed of the cacao tree, widely cultivated in tropical regions for use in chocolate, beverages, and various food products.
  • E. Kola
    Kola is a small town in Russia’s Murmansk Oblast, located near the city of Murmansk on the Kola Peninsula in the far northwest of the country.
  • 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_69d8b913351c8190932b6a426de04b41 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4fd8879e88190893f8da7c3529496 completed April 19, 2026, 4:06 p.m.
Created at: April 10, 2026, 10:34 a.m.