Triple

T18599830
Position Surface form Disambiguated ID Type / Status
Subject Yong Pung How E454588 entity
Predicate spouse P13 FINISHED
Object Mimi Wong
Mimi Wong is best known as the wife of the late Yong Pung How, who served as Chief Justice of Singapore.
 E1333459 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: Mimi Wong | Statement: [Yong Pung How, spouse, Mimi Wong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mimi Wong
Context triple: [Yong Pung How, spouse, Mimi Wong]
  • A. Maggie Wong
    Maggie Wong is a character from the comedy film "Balls of Fury," known as a skilled and determined ping-pong player who aids the protagonist in his quest.
  • B. Michelle Wong
    Michelle Wong is an actress known for her voice role in the animated feature film "Abominable."
  • C. Melissa Chiu
    Melissa Chiu is an Australian-born art historian and curator known for her leadership roles in major contemporary art institutions, including directing the Hirshhorn Museum and Sculpture Garden in Washington, D.C.
  • D. Amy Wong
    Amy Wong is a wealthy, klutzy intern and engineering student from Mars who serves as one of the core characters in the animated sci-fi comedy series Futurama.
  • E. Lindsay Wu
    Lindsay Wu is a biomedical scientist known for his research on aging and metabolism, particularly in the field of sirtuins and NAD⁺ biology.
  • 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: Mimi Wong
Triple: [Yong Pung How, spouse, Mimi Wong]
Generated description
Mimi Wong is best known as the wife of the late Yong Pung How, who served as Chief Justice of Singapore.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mimi Wong
Target entity description: Mimi Wong is best known as the wife of the late Yong Pung How, who served as Chief Justice of Singapore.
  • A. Maggie Wong
    Maggie Wong is a character from the comedy film "Balls of Fury," known as a skilled and determined ping-pong player who aids the protagonist in his quest.
  • B. Michelle Wong
    Michelle Wong is an actress known for her voice role in the animated feature film "Abominable."
  • C. Melissa Chiu
    Melissa Chiu is an Australian-born art historian and curator known for her leadership roles in major contemporary art institutions, including directing the Hirshhorn Museum and Sculpture Garden in Washington, D.C.
  • D. Amy Wong
    Amy Wong is a wealthy, klutzy intern and engineering student from Mars who serves as one of the core characters in the animated sci-fi comedy series Futurama.
  • E. Lindsay Wu
    Lindsay Wu is a biomedical scientist known for his research on aging and metabolism, particularly in the field of sirtuins and NAD⁺ biology.
  • 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_69d8d38ae7e081908a98df1251842402 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5475018548190a2f497081af7ce55 completed April 19, 2026, 9:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a050381bacc81909f416e8b9910f046 completed May 13, 2026, 11:04 p.m.
NEDg Description generation batch_6a0504ff8c6c8190a211b3f3e30229b4 completed May 13, 2026, 11:10 p.m.
NED2 Entity disambiguation (via description) batch_6a0505606bf88190afe635b2517d170b completed May 13, 2026, 11:12 p.m.
Created at: April 10, 2026, 11:45 a.m.