Triple

T607497
Position Surface form Disambiguated ID Type / Status
Subject Central Los Angeles E12026 entity
Predicate contains P35 FINISHED
Object Koreatown E3070 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: Koreatown | Statement: [Central Los Angeles, contains, Koreatown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Koreatown
Context triple: [Central Los Angeles, contains, Koreatown]
  • A. Koreatown chosen
    Koreatown is a vibrant Manhattan neighborhood known for its dense concentration of Korean restaurants, shops, and cultural businesses centered around West 32nd Street near the Empire State Building.
  • B. Seoul
    Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
  • C. Osan, South Korea
    Osan is a city in Gyeonggi Province, South Korea, known for its proximity to Osan Air Base and its role as a transportation and commercial hub south of Seoul.
  • D. Gwangju
    Gwangju is a major metropolitan city in southwestern South Korea known for its rich cultural heritage and pivotal role in the country’s pro-democracy movement.
  • E. Harajuku
    Harajuku is a vibrant Tokyo district famous for its youth culture, eclectic street fashion, and trendy shopping and entertainment spots.
  • 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_69a493309df48190a327f748e88049a6 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49df34abc8190a578c8c2ab3d28e4 completed March 1, 2026, 8:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69a52925321c8190a0d1df7a58096c4e completed March 2, 2026, 6:07 a.m.
Created at: March 1, 2026, 7:35 p.m.