Triple

T4248291
Position Surface form Disambiguated ID Type / Status
Subject Slovenian Littoral E95581 entity
Predicate hasPart P35 FINISHED
Object Goriška E381895 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: Goriška | Statement: [Slovenian Littoral, hasPart, Goriška]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Goriška
Context triple: [Slovenian Littoral, hasPart, Goriška]
  • A. Southeastern Slovenia
    Southeastern Slovenia is a statistical and historical region of Slovenia known for its rolling hills, vineyards, and the regional center Novo Mesto.
  • B. Sevnica
    Sevnica is a small town in central Slovenia known as the childhood home of former U.S. First Lady Melania Trump.
  • C. Goriška statistical region chosen
    The Goriška statistical region is an administrative and statistical area in western Slovenia, bordering Italy and centered around the city of Nova Gorica.
  • D. Gospić
    Gospić is a town in the Lika region of Croatia, known as the administrative center of Lika-Senj County and for its association with the birthplace of inventor Nikola Tesla in nearby Smiljan.
  • E. Radeče
    Radeče is a small town in central Slovenia, situated on the banks of the Sava River and known for its paper industry and scenic surroundings.
  • 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_69b3453d91548190b4d4ef8fe52aa2ac completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34e9cb71481909b4baa370193148f completed March 12, 2026, 11:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a87c033881908e0cf9fdfecaf36a completed March 14, 2026, 6:27 p.m.
Created at: March 12, 2026, 11:06 p.m.