Triple

T1993245
Position Surface form Disambiguated ID Type / Status
Subject Ai-Petri Mountain E43297 entity
Predicate hasNearbySettlement P4647 FINISHED
Object Koreiz E67751 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: Koreiz | Statement: [Ai-Petri Mountain, hasNearbySettlement, Koreiz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Koreiz
Context triple: [Ai-Petri Mountain, hasNearbySettlement, Koreiz]
  • A. Koreiz chosen
    Koreiz is a resort settlement on the southern coast of Crimea, known for its seaside location and historic villas.
  • B. Kōkyo
    Kōkyo is the primary residence of Japan’s Emperor, a historic palace complex and gardens located in central Tokyo.
  • C. Seoni
    Seoni is a town and district headquarters in the central Indian state of Madhya Pradesh, known for its proximity to Pench National Park and its association with Rudyard Kipling’s "The Jungle Book."
  • D. Kikuchi
    Kikuchi is a Japanese surname borne by various notable individuals across fields such as acting, sports, and academia.
  • E. Kamen
    Kamen is a surname most prominently associated with American inventor and entrepreneur Dean Kamen, known for creating the Segway and numerous medical devices.
  • 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_69a88714cf2c819081644be450b8356e completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb86275c88190bed869e9d3cf7ed5 completed March 7, 2026, 5:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae033a39d48190b6389e22de0d4418 completed March 8, 2026, 11:16 p.m.
Created at: March 4, 2026, 7:37 p.m.