Triple

T20871799
Position Surface form Disambiguated ID Type / Status
Subject Kajaki District E513910 entity
Predicate containsSettlement P847 FINISHED
Object Kajaki 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: Kajaki | Statement: [Kajaki District, containsSettlement, Kajaki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kajaki
Context triple: [Kajaki District, containsSettlement, Kajaki]
  • A. Kajaki chosen
    Kajaki is a town and district in Afghanistan’s Helmand Province, known for its strategic dam and as a focal point of intense military conflict during the Afghan War.
  • B. Kawki
    Kawki is an indigenous Andean language closely related to Aymara and spoken by a small number of people in Peru.
  • C. Kaiyukan
    Kaiyukan is a large, world-renowned public aquarium in Osaka, Japan, famous for its massive central tank and immersive marine life exhibits.
  • D. Kanajo
    Kanajo is a character or entity presented as the counterpart or parallel version of Manajo, typically within the same fictional or conceptual setting.
  • E. Kagayaki
    Kagayaki is the fastest limited-stop train service operating on Japan’s Hokuriku Shinkansen line between Tokyo and the Hokuriku region.
  • 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_69e0b4f675cc8190b4e745225b62eb66 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c4649cf88190b3ad946576aa46aa completed April 21, 2026, 12:27 a.m.
Created at: April 16, 2026, 12:45 p.m.