Triple

T2907708
Position Surface form Disambiguated ID Type / Status
Subject Minna Airport E63605 entity
Predicate serves P98 FINISHED
Object Minna E63605 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: Minna | Statement: [Minna Airport, serves, Minna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Minna
Context triple: [Minna Airport, serves, Minna]
  • A. Minna chosen
    Minna is a major city and administrative center in north-central Nigeria, known as the capital of Niger State and a regional hub for trade and transportation.
  • B. Miyabi
    Miyabi is a traditional Japanese-inspired lighting theme used on Tokyo Skytree, characterized by elegant, refined color schemes that evoke classical aesthetics.
  • C. Nozomi
    Nozomi is the fastest and most premium Shinkansen (bullet train) service operating on Japan’s Tokaido and Sanyo lines, known for its high speed and frequent departures between major cities like Tokyo and Osaka.
  • D. Hana
    Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
  • E. Hana
    Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
  • 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_69ab4c44ab448190b9411324e8a1fc1d completed March 6, 2026, 9:51 p.m.
NER Named-entity recognition batch_69abe0d1dcf881909b3ae58d7cdfd9cd completed March 7, 2026, 8:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69b08658407c8190ad7798590dd17ef9 completed March 10, 2026, 9 p.m.
Created at: March 6, 2026, 10:11 p.m.