Triple

T7492955
Position Surface form Disambiguated ID Type / Status
Subject Ha language E177051 entity
Predicate alternateName P39 FINISHED
Object Kiha E177052 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: Kiha | Statement: [Ha language, alternateName, Kiha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kiha
Context triple: [Ha language, alternateName, Kiha]
  • A. Kiha chosen
    Kiha is a Bantu language spoken by the Ha people of the Kigoma region in western Tanzania.
  • B. Hankyu 1300 series
    The Hankyu 1300 series is a Japanese electric multiple unit train type operated by Hankyu Railway, primarily used for commuter services in the Osaka area.
  • C. Hankyu 5300 series
    The Hankyu 5300 series is a Japanese electric multiple unit train type operated by Hankyu Railway, primarily used for commuter services in the Kansai region.
  • D. Rokkō Liner
    Rokkō Liner is an automated guideway transit line in Kobe, Japan, connecting the artificial island of Rokkō Island with the mainland.
  • E. Hankyu 3300 series
    The Hankyu 3300 series is a Japanese electric multiple unit train type operated by Hankyu Railway, primarily used for commuter services in the Osaka area.
  • 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_69c69f2583808190bd1a4936c42a5815 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f5784c908190b701959daf082625 completed March 27, 2026, 9:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c7c60b8819090f2c4b16332c557 completed March 28, 2026, 8:39 p.m.
Created at: March 27, 2026, 3:43 p.m.