Triple

T3637409
Position Surface form Disambiguated ID Type / Status
Subject Tan Hall E77105 entity
Predicate namedAfter P63 FINISHED
Object Charles C. Tan E77105 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: Charles C. Tan | Statement: [Tan Hall, namedAfter, Charles C. Tan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Charles C. Tan
Context triple: [Tan Hall, namedAfter, Charles C. Tan]
  • A. Charles C. Tan chosen
    Charles C. Tan was a notable benefactor and alumnus of the University of California, Berkeley, for whom the university’s chemical engineering building, Tan Hall, is named.
  • B. Cecil Chao
    Cecil Chao is a Hong Kong billionaire property developer best known internationally for offering large monetary rewards to any man who could successfully marry his lesbian daughter.
  • C. Larry Fong
    Larry Fong is an American cinematographer known for his visually striking work on major films such as "300," "Watchmen," and "Batman v Superman: Dawn of Justice."
  • D. Alfred Chuang
    Alfred Chuang is a Chinese-American technology entrepreneur best known as the co-founder and former CEO of enterprise software company BEA Systems.
  • E. Eugene Wong
    Eugene Wong is a computer scientist best known for his pioneering contributions to relational database theory and the development of early relational database systems.
  • 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_69ad85dd0be48190b738990cb20c4731 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc328e5e481909d26318c743bc84a completed March 8, 2026, 6:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69b44f23298481909d313d6b3f8013cd completed March 13, 2026, 5:53 p.m.
Created at: March 8, 2026, 3:24 p.m.