Triple

T19421822
Position Surface form Disambiguated ID Type / Status
Subject Someș River E485873 entity
Predicate mouth P407 FINISHED
Object Tisza River NE NERFINISHED

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: Tisza River | Statement: [Someș River, mouth, Tisza River]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tisza River
Context triple: [Someș River, mouth, Tisza River]
  • A. Tisza chosen
    The Tisza is one of Central Europe's significant rivers, flowing through several countries including Hungary before joining the Danube.
  • B. Szamos River
    The Szamos River is a Central European river flowing through Romania and Hungary, known for joining the Tisa River and draining part of the Eastern Carpathians.
  • C. Zala River
    The Zala River is a major river in western Hungary that drains a large catchment area before emptying into Lake Balaton.
  • D. Belá River
    The Belá River is a mountain river in northern Slovakia, known for its scenic course through the High Tatras region and popularity for rafting and outdoor recreation.
  • E. Kapós River
    The Kapós River is a watercourse in southwestern Hungary that flows through the Transdanubian region before joining the Sió River.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8d688f881909c85104a62e09d8a completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e632159d7081909d004544ec5992c0 completed April 20, 2026, 2:03 p.m.
Created at: April 10, 2026, 1:37 p.m.