Triple

T8982982
Position Surface form Disambiguated ID Type / Status
Subject Michael Nylander E214579 entity
Predicate playedForTeam P2168 FINISHED
Object Jokerit E66429 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: Jokerit | Statement: [Michael Nylander, playedForTeam, Jokerit]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jokerit
Context triple: [Michael Nylander, playedForTeam, Jokerit]
  • A. Jokerit chosen
    Jokerit is a professional ice hockey club from Helsinki, Finland, known as one of the country’s most successful and popular teams.
  • B. HIFK Helsinki
    HIFK Helsinki is a prominent professional ice hockey club from Helsinki, Finland, known as one of the country’s most successful and traditional teams in the Liiga.
  • C. Ilves
    Ilves is a prominent Finnish sports club from Tampere best known for its ice hockey team competing in the country’s top professional league.
  • D. Joensuu Wolves
    Joensuu Wolves is a sports club based in Joensuu, Finland, best known for representing the city in competitive team sports.
  • E. Kärpät Oulu
    Kärpät Oulu is a prominent Finnish professional ice hockey club from Oulu, known as one of the most successful teams in the Liiga.
  • 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_69ca839f76bc8190a4b7123cdd682199 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc67a891e881909e4b84ed82491651 completed April 1, 2026, 12:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfd0b3ce0c81908a7b4297e0867cc9 completed April 3, 2026, 2:37 p.m.
Created at: March 30, 2026, 7:03 p.m.