Triple

T15427505
Position Surface form Disambiguated ID Type / Status
Subject Segyo-dong E369549 entity
Predicate locatedIn P40 FINISHED
Object Osan E223589 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: Osan | Statement: [Segyo-dong, locatedIn, Osan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Osan
Context triple: [Segyo-dong, locatedIn, Osan]
  • A. Osan chosen
    Osan is a city in Gyeonggi Province, South Korea, known for its proximity to Osan Air Base and its role as a regional transportation and commercial hub.
  • B. Oza
    "Oza" is a prominent poetic work by Russian poet Andrei Voznesensky, reflecting his innovative style and experimental approach to verse.
  • C. Oimachi
    Oimachi is a commercial and residential district in Tokyo known for its busy train hub, shopping streets, and convenient access to central Shinagawa and other parts of the city.
  • D. Ozaki
    Ozaki is a Japanese surname borne by various notable figures in politics, literature, and the arts.
  • E. Dairen
    Dairen, now known as Dalian, is a major port city in northeastern China that historically served as an important strategic and commercial hub under various foreign leases and administrations.
  • 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_69d85a1849f48190bf898068b2806fae completed April 10, 2026, 2:02 a.m.
NER Named-entity recognition batch_69e03ec31f4881908b26ff7c381d7bc9 completed April 16, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3d40d3388190b1bd724238f928b1 completed May 9, 2026, 1:57 p.m.
Created at: April 10, 2026, 3:20 a.m.