Triple

T16852687
Position Surface form Disambiguated ID Type / Status
Subject LG Twin Towers E409711 entity
Predicate KoreanName P17869 FINISHED
Object LG트윈타워 E409711 NE FINISHED

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: LG트윈타워 | Statement: [LG Twin Towers, KoreanName, LG트윈타워]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LG트윈타워
Context triple: [LG Twin Towers, KoreanName, LG트윈타워]
  • A. LG Twin Towers chosen
    LG Twin Towers is the landmark twin-skyscraper headquarters complex of LG Corporation located in Seoul, South Korea.
  • B. Hanbit Tower
    Hanbit Tower is a prominent landmark and observation tower in Daejeon, South Korea, originally built for the Daejeon Expo and now serving as a symbol of science and technology in the city.
  • C. Lotte
    Lotte is a common diminutive form of the given name Charlotte, used in several European languages.
  • D. Lotte
    Lotte is a municipality in the German state of North Rhine-Westphalia, known for its location near Osnabrück and its local football club Sportfreunde Lotte.
  • E. KL Tower
    KL Tower is a prominent telecommunications and observation tower in Kuala Lumpur, Malaysia, known for its panoramic city views and iconic skyline presence.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d88395e6c88190b22730f335107c14 completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b37abadc81909d02d329403497d6 completed April 18, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00bb216fac81909d401c6b9911d1e0 completed May 10, 2026, 5:06 p.m.
Created at: April 10, 2026, 5:24 a.m.