Triple
T12889956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benson Fong |
E308331
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Gloria Fong
Gloria Fong was the wife of American character actor and restaurateur Benson Fong.
|
E1009268
|
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: Gloria Fong | Statement: [Benson Fong, spouse, Gloria Fong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gloria Fong Context triple: [Benson Fong, spouse, Gloria Fong]
-
A.
Edith Chao
Edith Chao was the wife of Chinese warlord and political figure Zhang Xueliang, accompanying him through his long years of house arrest and exile.
-
B.
Michelle Yee
Michelle Yee is an American philanthropist and investor best known as the wife of LinkedIn co-founder Reid Hoffman.
-
C.
Julie Chu
Julie Chu is an American ice hockey player and four-time Olympian renowned as one of the most accomplished figures in U.S. women's hockey history.
-
D.
Rachel Fong
Rachel Fong is a researcher in machine learning and reinforcement learning, known for her work on the Hindsight Experience Replay technique.
-
E.
Linda Cho
Linda Cho is a Tony Award–winning costume designer known for her work on major Broadway productions and other theatrical performances.
- 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: Gloria Fong Triple: [Benson Fong, spouse, Gloria Fong]
Generated description
Gloria Fong was the wife of American character actor and restaurateur Benson Fong.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gloria Fong Target entity description: Gloria Fong was the wife of American character actor and restaurateur Benson Fong.
-
A.
Edith Chao
Edith Chao was the wife of Chinese warlord and political figure Zhang Xueliang, accompanying him through his long years of house arrest and exile.
-
B.
Michelle Yee
Michelle Yee is an American philanthropist and investor best known as the wife of LinkedIn co-founder Reid Hoffman.
-
C.
Julie Chu
Julie Chu is an American ice hockey player and four-time Olympian renowned as one of the most accomplished figures in U.S. women's hockey history.
-
D.
Rachel Fong
Rachel Fong is a researcher in machine learning and reinforcement learning, known for her work on the Hindsight Experience Replay technique.
-
E.
Linda Cho
Linda Cho is a Tony Award–winning costume designer known for her work on major Broadway productions and other theatrical performances.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714581988190afc720ffd7797860 |
completed | April 10, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a5598ad08190bad57ccfb4e4e2b6 |
completed | May 3, 2026, 1:31 a.m. |
| NEDg | Description generation | batch_69f6a62ca8b48190b3a1483801fe5bb9 |
completed | May 3, 2026, 1:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6a82cdf648190aaad99d2951f22b3 |
completed | May 3, 2026, 1:43 a.m. |
Created at: April 9, 2026, 5:39 p.m.