Triple

T4317474
Position Surface form Disambiguated ID Type / Status
Subject Jarosław Kaczyński E96427 entity
Predicate givenName P17 FINISHED
Object Jarosław E284545 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: Jarosław | Statement: [Jarosław Kaczyński, givenName, Jarosław]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jarosław
Context triple: [Jarosław Kaczyński, givenName, Jarosław]
  • A. Jarosław chosen
    Jarosław is a historic town in southeastern Poland known for its well-preserved Old Town and role as a former important trade center.
  • B. Jasło
    Jasło is a small town in southeastern Poland, known as part of the historical region of Galicia and for its cultural and educational traditions.
  • C. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • D. Ostrołęka
    Ostrołęka is a town in east-central Poland known for its historical role in the Napoleonic Wars and as a local industrial and administrative center.
  • E. Lubin
    Lubin is a town in southwestern Poland known for its copper mining industry and location within the Lower Silesian region.
  • 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_69b345422aac81909ddbadae437d122e completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b350f733888190866611fcdea97a5f completed March 12, 2026, 11:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69bde04d4bbc8190891aecc8244b566c completed March 21, 2026, 12:03 a.m.
Created at: March 12, 2026, 11:12 p.m.