Triple

T10252923
Position Surface form Disambiguated ID Type / Status
Subject Anastasia E240387 entity
Predicate costumeDesigner P184 FINISHED
Object Linda Cho E561358 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: Linda Cho | Statement: [Anastasia, costumeDesigner, Linda Cho]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Linda Cho
Context triple: [Anastasia, costumeDesigner, Linda Cho]
  • A. Linda Cho chosen
    Linda Cho is a Tony Award–winning costume designer known for her work on major Broadway productions and other theatrical performances.
  • B. Linda Park
    Linda Park is a Korean-American actress best known for playing communications officer Hoshi Sato on the television series Star Trek: Enterprise.
  • C. Margaret Chung
    Margaret Chung was a pioneering Chinese American physician and surgeon, widely regarded as the first Chinese American woman doctor in the United States and known for her influential role in supporting U.S. military personnel during World War II.
  • D. Sandra Chung
    Sandra Chung is an American linguist known for her influential work on syntax, Austronesian languages, and the interface between grammar and semantics.
  • E. Karen Kwan
    Karen Kwan is an American figure skater and the older sister of Olympic medalist Michelle Kwan.
  • 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_69d381a7e198819090280d5ab885d59e completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d23f106c81909c1ce20a2ffa86ea completed April 7, 2026, 9:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6f7c6aca08190b15e3790cad12532 completed April 9, 2026, 12:50 a.m.
Created at: April 6, 2026, 11:29 a.m.