Triple
T13138371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosalind |
E312143
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Rosalind Chao
Rosalind Chao is an American actress best known for her roles in "The Joy Luck Club" and the "Star Trek" franchise.
|
E1024756
|
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: Rosalind Chao | Statement: [Rosalind, hasNotableBearer, Rosalind Chao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosalind Chao Context triple: [Rosalind, hasNotableBearer, Rosalind Chao]
-
A.
Fay Chang
Fay Chang is a computer scientist known for co-authoring the influential Google Bigtable paper on large-scale distributed storage systems.
-
B.
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.
-
C.
Jane X. Luu
Jane X. Luu is an astronomer best known for co-discovering the first Kuiper Belt Object beyond Pluto, helping to revolutionize our understanding of the outer Solar System.
-
D.
Linda Cho
Linda Cho is a Tony Award–winning costume designer known for her work on major Broadway productions and other theatrical performances.
-
E.
Rosalie Chiang
Rosalie Chiang is an American actress best known for voicing the main character, Meilin "Mei" Lee, in Pixar's animated film "Turning Red."
- 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: Rosalind Chao Triple: [Rosalind, hasNotableBearer, Rosalind Chao]
Generated description
Rosalind Chao is an American actress best known for her roles in "The Joy Luck Club" and the "Star Trek" franchise.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rosalind Chao Target entity description: Rosalind Chao is an American actress best known for her roles in "The Joy Luck Club" and the "Star Trek" franchise.
-
A.
Fay Chang
Fay Chang is a computer scientist known for co-authoring the influential Google Bigtable paper on large-scale distributed storage systems.
-
B.
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.
-
C.
Jane X. Luu
Jane X. Luu is an astronomer best known for co-discovering the first Kuiper Belt Object beyond Pluto, helping to revolutionize our understanding of the outer Solar System.
-
D.
Linda Cho
Linda Cho is a Tony Award–winning costume designer known for her work on major Broadway productions and other theatrical performances.
-
E.
Rosalie Chiang
Rosalie Chiang is an American actress best known for voicing the main character, Meilin "Mei" Lee, in Pixar's animated film "Turning Red."
- 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_69d806a9fe888190b081e2d9ea665d6c |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d981b6a4348190b9922ed255759078 |
completed | April 10, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eae2c3848190b062fa8da7dc8a92 |
completed | May 3, 2026, 6:27 a.m. |
| NEDg | Description generation | batch_69f6f01e9c488190ad52280903c074da |
completed | May 3, 2026, 6:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6f0e5f94c81908f4707913dc5dc92 |
completed | May 3, 2026, 6:53 a.m. |
Created at: April 9, 2026, 9:09 p.m.