Triple

T1983344
Position Surface form Disambiguated ID Type / Status
Subject Saint-Étienne E43079 entity
Predicate twinnedWith P1072 FINISHED
Object Katowice E32146 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: Katowice | Statement: [Saint-Étienne, twinnedWith, Katowice]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Katowice
Context triple: [Saint-Étienne, twinnedWith, Katowice]
  • A. Katowice chosen
    Katowice is a major industrial and cultural city in southern Poland, known as the capital of the Silesian region.
  • B. Wrocław
    Wrocław is a major historic city in southwestern Poland, known for its picturesque Old Town, numerous bridges over the Oder River, and role as a cultural and academic center.
  • C. Gliwice
    Gliwice is a historic industrial and academic city in southern Poland’s Silesian region, known for its engineering university and the landmark Gliwice Radio Tower.
  • D. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • E. Chorzów
    Chorzów is an industrial city in southern Poland’s Silesian region, known for its heavy industry heritage and the extensive Silesian Park.
  • 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_69a88713ddc88190a969715658ebe7a8 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb820815481908aac6d89b437225b completed March 7, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69b44eb4706c8190870d99abc140973d completed March 13, 2026, 5:51 p.m.
Created at: March 4, 2026, 7:37 p.m.