Triple

T16371632
Position Surface form Disambiguated ID Type / Status
Subject Most E397576 entity
Predicate regionCapitalOf P204 FINISHED
Object Most District E1202537 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: Most District | Statement: [Most, regionCapitalOf, Most District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Most District
Context triple: [Most, regionCapitalOf, Most District]
  • A. Most District chosen
    Most District is an administrative district in the Ústí nad Labem Region of the Czech Republic, known for its industrial landscape and historical lignite mining.
  • B. Union District
    Union District was a historical judicial and administrative district in South Carolina that existed in the late 18th and early 19th centuries before the state reorganized its counties.
  • C. Central Districts
    Central Districts is a New Zealand domestic first-class cricket team representing several central regions of the country in national competitions.
  • D. Mid-City
    Mid-City is a historic, largely residential New Orleans neighborhood known for its diverse community, local eateries, and proximity to City Park and the streetcar line.
  • E. Mid-City
    Mid-City is a central Los Angeles neighborhood known for its diverse residential communities and proximity to major city thoroughfares and cultural districts.
  • 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_69d87f2778dc8190aa95c7572db127e6 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2ff420d04819096ff12e08edf2f8b completed April 18, 2026, 3:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a002dc44c508190bf87b437d1447db6 completed May 10, 2026, 7:03 a.m.
Created at: April 10, 2026, 5:08 a.m.