Triple

T15099973
Position Surface form Disambiguated ID Type / Status
Subject Sajó River E360637 entity
Predicate knownAs P39 FINISHED
Object Sajó E215781 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: Sajó | Statement: [Sajó River, knownAs, Sajó]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sajó
Context triple: [Sajó River, knownAs, Sajó]
  • A. Sajó chosen
    Sajó is a river in Central Europe that flows through Slovakia and northeastern Hungary before joining the Tisza River.
  • B. Bodrogköz
    Bodrogköz is a low-lying, marshy region in northeastern Hungary known for its riverine landscapes, wetlands, and traditional rural settlements.
  • C. Trencsén
    Trencsén is a historic town in present-day Slovakia, known for its medieval castle and its role as an important regional center in the former Upper Hungary.
  • D. Bácska
    Bácska is a historical region in the Pannonian Plain, today divided between northern Serbia and southern Hungary, known for its multicultural population and agricultural importance.
  • E. Borsod
    Borsod is a historical region in northeastern Hungary that once formed its own county and now lends its name to the modern Borsod-Abaúj-Zemplén County.
  • 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_69d85a035aa88190b52a139d3a1b7b6d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00550007481909e02ee1d597a4d37 completed April 15, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69feb7e6fc7c8190b517a7daa567d67c completed May 9, 2026, 4:28 a.m.
Created at: April 10, 2026, 3:04 a.m.