Triple
T16635375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eat a Bowl of Tea |
E404183
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Judy Ongg
Judy Ongg is a Taiwanese-Japanese actress and singer known for her film and television roles across East Asia and her successful music career.
|
E1224182
|
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: Judy Ongg | Statement: [Eat a Bowl of Tea, castMember, Judy Ongg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Judy Ongg Context triple: [Eat a Bowl of Tea, castMember, Judy Ongg]
-
A.
Debbie Ong
Debbie Ong is a Singaporean judge who serves as the Presiding Judge of the State Courts of Singapore, overseeing the administration and operations of the country's primary trial courts.
-
B.
Rachel Fong
Rachel Fong is a researcher in machine learning and reinforcement learning, known for her work on the Hindsight Experience Replay technique.
-
C.
Yvonne Chu
Yvonne Chu is the wife of Nobel Prize–winning physicist and former U.S. Secretary of Energy Steven Chu.
-
D.
Karen Kwan
Karen Kwan is an American figure skater and the older sister of Olympic medalist Michelle Kwan.
-
E.
Laureen Chew
Laureen Chew is an actress best known for her role in the influential 1982 Asian American independent film "Chan Is Missing."
- 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: Judy Ongg Triple: [Eat a Bowl of Tea, castMember, Judy Ongg]
Generated description
Judy Ongg is a Taiwanese-Japanese actress and singer known for her film and television roles across East Asia and her successful music career.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Judy Ongg Target entity description: Judy Ongg is a Taiwanese-Japanese actress and singer known for her film and television roles across East Asia and her successful music career.
-
A.
Debbie Ong
Debbie Ong is a Singaporean judge who serves as the Presiding Judge of the State Courts of Singapore, overseeing the administration and operations of the country's primary trial courts.
-
B.
Rachel Fong
Rachel Fong is a researcher in machine learning and reinforcement learning, known for her work on the Hindsight Experience Replay technique.
-
C.
Yvonne Chu
Yvonne Chu is the wife of Nobel Prize–winning physicist and former U.S. Secretary of Energy Steven Chu.
-
D.
Karen Kwan
Karen Kwan is an American figure skater and the older sister of Olympic medalist Michelle Kwan.
-
E.
Laureen Chew
Laureen Chew is an actress best known for her role in the influential 1982 Asian American independent film "Chan Is Missing."
- 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e378e999d48190bff680040dbc883d |
completed | April 18, 2026, 12:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a007dc05bd881909c6b2e0d95622aa1 |
completed | May 10, 2026, 12:44 p.m. |
| NEDg | Description generation | batch_6a007e1909b88190ad2587b5d5433e2e |
completed | May 10, 2026, 12:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a007eda229c8190a6b99400141cf0b6 |
completed | May 10, 2026, 12:49 p.m. |
Created at: April 10, 2026, 5:17 a.m.