Triple

T4144317
Position Surface form Disambiguated ID Type / Status
Subject Central Lithuania E89346 entity
Predicate containsCity P294 FINISHED
Object Raseiniai E412905 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: Raseiniai | Statement: [Central Lithuania, containsCity, Raseiniai]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Raseiniai
Context triple: [Central Lithuania, containsCity, Raseiniai]
  • A. Raseiniai chosen
    Raseiniai is a historic town in central Lithuania known for its role in regional trade and its cultural heritage within Kaunas County.
  • B. Sudeikis
    Sudeikis is the surname of American actor, comedian, and writer Jason Sudeikis, known for his work on Saturday Night Live and the series Ted Lasso.
  • C. Kaišiadorys
    Kaišiadorys is a small Lithuanian town known as an important railway junction and administrative center in central Lithuania.
  • D. Rausu
    Rausu is a small coastal town on Japan’s Shiretoko Peninsula, known for its rich marine wildlife, drift ice sightseeing, and access to the remote natural landscapes of eastern Hokkaido.
  • E. Aukštaitija
    Aukštaitija is a historical and ethnographic region in northeastern Lithuania known for its lakes, forests, and strong preservation of traditional Lithuanian culture and dialects.
  • 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_69aed95785788190ae75bcf0cd1cafdf completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af025d2984819095f299327cc399d5 completed March 9, 2026, 5:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69b576d2f1788190847d38a384abbe67 completed March 14, 2026, 2:55 p.m.
Created at: March 9, 2026, 3:43 p.m.