Triple

T10671015
Position Surface form Disambiguated ID Type / Status
Subject Frankfurt skyline E251483 entity
Predicate nicknameDerivedFrom P46615 FINISHED
Object River Main E113003 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: River Main | Statement: [Frankfurt skyline, nicknameDerivedFrom, River Main]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: River Main
Context triple: [Frankfurt skyline, nicknameDerivedFrom, River Main]
  • A. River Main chosen
    The River Main is a major waterway in central Germany that flows through cities such as Frankfurt before joining the Rhine.
  • B. Spring River
    Spring River is a tributary of the Arkansas River that flows through parts of Kansas, Missouri, and Oklahoma in the central United States.
  • C. Train River
    The Train River is a smaller watercourse in Belgium that serves as a tributary within the Dyle River basin.
  • D. Kako River
    The Kako River is a river in Japan whose name was used for the Imperial Japanese Navy cruiser Kako.
  • E. Long River
    Long River is the English translation of the Chinese name for the Yangtze, Asia’s longest river and a major waterway in China.
  • 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_69d6aa5b0d2881909584b20efc5877f0 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6f8648a248190a3bd284c569152e4 completed April 9, 2026, 12:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69de225f57948190aaf2954bce91f752 completed April 14, 2026, 11:17 a.m.
Created at: April 8, 2026, 9:09 p.m.