Triple

T5964194
Position Surface form Disambiguated ID Type / Status
Subject MLS Cup 1999 E132711 entity
Predicate homeTeamManager P42666 FINISHED
Object Thomas Rongen E156867 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: Thomas Rongen | Statement: [MLS Cup 1999, homeTeamManager, Thomas Rongen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thomas Rongen
Context triple: [MLS Cup 1999, homeTeamManager, Thomas Rongen]
  • A. Thomas Rongen chosen
    Thomas Rongen is a Dutch-American soccer coach and former player known for his extensive coaching career in Major League Soccer and with various U.S. national youth teams.
  • B. John Van Tongeren
    John Van Tongeren is a film and television composer known for scoring movies such as "Miss Congeniality 2: Armed and Fabulous."
  • C. Pieter R. de Jong
    Pieter R. de Jong is a Dutch professional who studied at Utrecht University and is recognized as a notable alumnus for his contributions in his field.
  • D. Jan T. Kleyna
    Jan T. Kleyna is an astronomer known for discovering outer irregular moons of Jupiter, including Taygete.
  • E. Don Roos
    Don Roos is an American screenwriter and film director known for his sharp, darkly comedic dramas such as "The Opposite of Sex" and "Happy Endings."
  • 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_69c0086c2364819091e9fe2f58fa2517 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c03a0240cc81909d7c75c7e6d630f7 completed March 22, 2026, 6:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69c12505bb8c8190a2f509e9615d0dcd completed March 23, 2026, 11:33 a.m.
Created at: March 22, 2026, 4:03 p.m.