Triple

T616272
Position Surface form Disambiguated ID Type / Status
Subject Tan Hall E14410 entity
Predicate namedAfter P63 FINISHED
Object Charles C. Tan
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.
 E77105 NE FINISHED

How this triple was built (4 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. Kenneth Hsu
    Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
  • B. John Cheng
    John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
  • C. Yu-Chi Ho
    Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
  • D. T.H. Chan
    T.H. Chan was a Hong Kong real estate developer and philanthropist whose family’s major donation led to Harvard’s public health school bearing his name.
  • E. Joe Tsai
    Joe Tsai is a Taiwanese-Canadian billionaire businessman and co-founder of Alibaba Group who owns the NBA’s Brooklyn Nets.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Charles C. Tan
Triple: [Tan Hall, namedAfter, Charles C. Tan]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Charles C. Tan
Target entity description: 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.
  • A. Kenneth Hsu
    Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
  • B. John Cheng
    John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
  • C. Yu-Chi Ho
    Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
  • D. T.H. Chan
    T.H. Chan was a Hong Kong real estate developer and philanthropist whose family’s major donation led to Harvard’s public health school bearing his name.
  • E. Joe Tsai
    Joe Tsai is a Taiwanese-Canadian billionaire businessman and co-founder of Alibaba Group who owns the NBA’s Brooklyn Nets.
  • F. None of above. chosen

Provenance (5 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_69a4934b17c881909ace8270e8ddd202 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49e22f3688190a512bec3f0347814 completed March 1, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5554b4f888190b9b64ece37087bf4 completed March 2, 2026, 9:15 a.m.
NEDg Description generation batch_69a555ae08b88190aad64ec7923437ef completed March 2, 2026, 9:17 a.m.
NED2 Entity disambiguation (via description) batch_69a556669878819098816d2221a3fd3d completed March 2, 2026, 9:20 a.m.
Created at: March 1, 2026, 7:35 p.m.