Triple

T14968694
Position Surface form Disambiguated ID Type / Status
Subject Działdowo E373258 entity
Predicate hasSportsClub P346 FINISHED
Object Start Działdowo E373258 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: Start Działdowo | Statement: [Działdowo, hasSportsClub, Start Działdowo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Start Działdowo
Context triple: [Działdowo, hasSportsClub, Start Działdowo]
  • A. Działdowo chosen
    Działdowo is a town in northern Poland known for its historical significance and location within the Warmian-Masurian Voivodeship.
  • B. Zadwórze
    Zadwórze is a village in present-day western Ukraine known primarily as the site of the 1920 Battle of Zadwórze during the Polish–Soviet War.
  • C. Działoszyce
    Działoszyce is a small historic town in south-central Poland, known for its former Jewish community and traditional urban layout.
  • D. Darłowo
    Darłowo is a historic coastal town in northwestern Poland, known for its medieval castle and seaside resort on the Baltic Sea.
  • E. Gołdap
    Gołdap is a town in northeastern Poland known for its spa facilities, scenic lakes and forests, and proximity to the Russian border.
  • 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_69d85ccbbcd48190acb56e7cf104d8ad completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded6e44cb0819096e09f8026ef8174 completed April 15, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe8be4b0a88190989022108a58370d completed May 9, 2026, 1:20 a.m.
Created at: April 10, 2026, 2:48 a.m.