Triple

T16398663
Position Surface form Disambiguated ID Type / Status
Subject Jürgen Klinsmann E398251 entity
Predicate memberOfSportsTeam P330 FINISHED
Object AS Monaco E384808 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: AS Monaco | Statement: [Jürgen Klinsmann, memberOfSportsTeam, AS Monaco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AS Monaco
Context triple: [Jürgen Klinsmann, memberOfSportsTeam, AS Monaco]
  • A. AS Monaco chosen
    AS Monaco is a prominent football club based in the Principality of Monaco, known for competing in France's top division and developing numerous world-class players.
  • B. Monaco
    Monaco was the original name of Crypto.com, a cryptocurrency and financial services platform known for its crypto-backed payment cards and trading app.
  • C. Monaco
    Monaco is a small sovereign city-state on the French Riviera known for its wealth, luxury tourism, and status as a major tax haven and gambling hub.
  • D. Monte Carlo, Monaco
    Monte Carlo, Monaco is a luxurious Mediterranean district famed for its casinos, upscale resorts, and status as a playground for the wealthy and international elite.
  • E. San Marino
    San Marino is a small, affluent residential city in Los Angeles County, California, known for its high-ranking schools and the Huntington Library, Art Museum, and Botanical Gardens.
  • 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_69d87f2950248190bc8ad9b9bebdc8c8 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e327cdc62481909de144b09a921e63 completed April 18, 2026, 6:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00357838a88190be88c51f454be6eb completed May 10, 2026, 7:36 a.m.
Created at: April 10, 2026, 5:09 a.m.