Triple

T6587511
Position Surface form Disambiguated ID Type / Status
Subject Chiba Lotte Marines E159262 entity
Predicate formerName P65 FINISHED
Object Lotte Orions E168850 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: Lotte Orions | Statement: [Chiba Lotte Marines, formerName, Lotte Orions]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lotte Orions
Context triple: [Chiba Lotte Marines, formerName, Lotte Orions]
  • A. Lotte Orions chosen
    Lotte Orions was a Japanese professional baseball team in Nippon Professional Baseball, known as a predecessor to the Chiba Lotte Marines.
  • B. Leona Samish
    Leona Samish is the central character in Arthur Laurents and Richard Rodgers’ musical "Do I Hear a Waltz?", typically portrayed as a lonely American tourist in Venice searching for love and self-discovery.
  • C. Odette Yustman
    Odette Yustman is an American actress known for her roles in films such as Cloverfield and The Unborn, as well as various television series.
  • D. Orly Sud
    Orly Sud is the former name of Orly 4, a terminal facility at Paris Orly Airport in France.
  • E. Nellie Kim
    Nellie Kim is a former Soviet artistic gymnast renowned for her multiple Olympic gold medals in the 1970s and for pioneering difficult tumbling and vaulting skills.
  • 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_69c688366ce8819083f8883983c0df92 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6aeafd2848190bb51e3ea9711c803 completed March 27, 2026, 4:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6d5794f388190a04f4f9c9e628f22 completed March 27, 2026, 7:07 p.m.
Created at: March 27, 2026, 1:55 p.m.